





import math

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



def A_     (		A__		,				A__		,				A__ = 1		,				A__ = 1		,				A__ = 1.0E4		,				A__ = False		,				A__ = 1.0		,				)  ->		List[str]:
       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F'Embedding dimension {embedding_dim} should be even'
       a__						:     List[str]			=    float(embedding_dim // 2    )
       a__						:     List[Any]			=    math.log(max_timescale / min_timescale    ) / (num_timescales - freq_shift)
       a__						:     Dict			=    min_timescale * jnp.exp(jnp.arange(_a		,				dtype=jnp.floataa    ) * -log_timescale_increment    )
       a__						:     Optional[Any]			=    jnp.expand_dims(_a		,				1    ) * jnp.expand_dims(_a		,				0    )

       # scale embeddings
       a__						:     Any			=    scale * emb

       if flip_sin_to_cos:
              a__						:     Optional[int]			=    jnp.concatenate([jnp.cos(_a    ), jnp.sin(_a    )]		,				axis=1    )
       else:
              a__						:     Union[str, Any]			=    jnp.concatenate([jnp.sin(_a    ), jnp.cos(_a    )]		,				axis=1    )
       a__						:     Dict			=    jnp.reshape(_a		,				[jnp.shape(_a    )[0], embedding_dim]    )
       return signal





class 			A__   (							nn.Module     ):




   """simple docstring"""



   __A       :    List[str]	  =  3_2
   __A       :    Any	  =  jnp.floataa

   @nn.compact
   def __call__( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Tuple			=    nn.Dense(self.time_embed_dim ,  dtype=self.dtype ,  name='linear_1')(lowerCamelCase_)
          a__						:     int			=    nn.silu(lowerCamelCase_)
          a__						:     Optional[int]			=    nn.Dense(self.time_embed_dim ,  dtype=self.dtype ,  name='linear_2')(lowerCamelCase_)
          return temb





class 			A__   (							nn.Module     ):




   """simple docstring"""



   __A       :    Union[str, Any]	  =  3_2
   __A       :    Tuple	  =  False
   __A       :    List[Any]	  =  1

   @nn.compact
   def __call__( self ,  lowercase)      ->						List[str]:







          '''simple docstring'''

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase				:	List[str]      						=  {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Tuple      						=  ["""CLIPTokenizerFast"""]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""CLIPFeatureExtractor"""]
    lowercase				:	Union[str, Any]      						=  ["""CLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Dict      						=  [
        """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CLIPModel""",
        """CLIPPreTrainedModel""",
        """CLIPTextModel""",
        """CLIPTextModelWithProjection""",
        """CLIPVisionModel""",
        """CLIPVisionModelWithProjection""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  [
        """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCLIPModel""",
        """TFCLIPPreTrainedModel""",
        """TFCLIPTextModel""",
        """TFCLIPVisionModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Any      						=  [
        """FlaxCLIPModel""",
        """FlaxCLIPPreTrainedModel""",
        """FlaxCLIPTextModel""",
        """FlaxCLIPTextPreTrainedModel""",
        """FlaxCLIPVisionModel""",
        """FlaxCLIPVisionPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPOnnxConfig,
        CLIPTextConfig,
        CLIPVisionConfig,
    )
    from .processing_clip import CLIPProcessor
    from .tokenization_clip import CLIPTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_clip_fast import CLIPTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clip import CLIPFeatureExtractor
        from .image_processing_clip import CLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPTextModelWithProjection,
            CLIPVisionModel,
            CLIPVisionModelWithProjection,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_clip import (
            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCLIPModel,
            TFCLIPPreTrainedModel,
            TFCLIPTextModel,
            TFCLIPVisionModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            FlaxCLIPTextPreTrainedModel,
            FlaxCLIPVisionModel,
            FlaxCLIPVisionPreTrainedModel,
        )


else:
    import sys

    lowercase				:	List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


