


import math

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


def      a	(							SCREAMING_SNAKE_CASE_      :		jnp.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		float = 1							,			SCREAMING_SNAKE_CASE_      :		float = 1							,			SCREAMING_SNAKE_CASE_      :		float = 1.0E4							,			SCREAMING_SNAKE_CASE_      :		bool = False							,			SCREAMING_SNAKE_CASE_      :		float = 1.0							,			):






       """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"""
       UpperCamelCase    :	Dict   	=		float(embedding_dim // 2       )
       UpperCamelCase    :	Union[str, Any]   	=		math.log(max_timescale / min_timescale       ) / (num_timescales - freq_shift)
       UpperCamelCase    :	Tuple   	=		min_timescale * jnp.exp(jnp.arange(SCREAMING_SNAKE_CASE_							,			dtype=jnp.floataa       ) * -log_timescale_increment       )
       UpperCamelCase    :	List[str]   	=		jnp.expand_dims(SCREAMING_SNAKE_CASE_							,			1       ) * jnp.expand_dims(SCREAMING_SNAKE_CASE_							,			0       )

       # scale embeddings
       UpperCamelCase    :	Optional[Any]   	=		scale * emb

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



class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''
       __UpperCamelCase      :     int							=					32
       __UpperCamelCase      :     jnp.dtype							=					jnp.floataa






       @nn.compact
       def __call__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		nn.Dense(self.time_embed_dim ,					dtype=self.dtype ,					name='''linear_1'''						)(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		nn.silu(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		nn.Dense(self.time_embed_dim ,					dtype=self.dtype ,					name='''linear_2'''						)(__SCREAMING_SNAKE_CASE						)
              return temb




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''
       __UpperCamelCase      :     int							=					32
       __UpperCamelCase      :     bool							=					False
       __UpperCamelCase      :     float							=					1






       @nn.compact
       def __call__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              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						)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase : str 			=				logging.get_logger(__name__)

__UpperCAmelCase : Dict 			=				{
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					"yolos"






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=768 ,					__SCREAMING_SNAKE_CASE=12 ,					__SCREAMING_SNAKE_CASE=12 ,					__SCREAMING_SNAKE_CASE=3_072 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1e-12 ,					__SCREAMING_SNAKE_CASE=[512, 864] ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=100 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=0.1 ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Tuple   	=		hidden_size
              UpperCamelCase    :	List[Any]   	=		num_hidden_layers
              UpperCamelCase    :	int   	=		num_attention_heads
              UpperCamelCase    :	Dict   	=		intermediate_size
              UpperCamelCase    :	Dict   	=		hidden_act
              UpperCamelCase    :	int   	=		hidden_dropout_prob
              UpperCamelCase    :	Any   	=		attention_probs_dropout_prob
              UpperCamelCase    :	Optional[Any]   	=		initializer_range
              UpperCamelCase    :	List[Any]   	=		layer_norm_eps
              UpperCamelCase    :	int   	=		image_size
              UpperCamelCase    :	Any   	=		patch_size
              UpperCamelCase    :	str   	=		num_channels
              UpperCamelCase    :	str   	=		qkv_bias
              UpperCamelCase    :	Tuple   	=		num_detection_tokens
              UpperCamelCase    :	List[Any]   	=		use_mid_position_embeddings
              UpperCamelCase    :	Dict   	=		auxiliary_loss
              # Hungarian matcher
              UpperCamelCase    :	Optional[Any]   	=		class_cost
              UpperCamelCase    :	Union[str, Any]   	=		bbox_cost
              UpperCamelCase    :	Any   	=		giou_cost
              # Loss coefficients
              UpperCamelCase    :	List[Any]   	=		bbox_loss_coefficient
              UpperCamelCase    :	Union[str, Any]   	=		giou_loss_coefficient
              UpperCamelCase    :	Dict   	=		eos_coefficient




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     List[str]							=					version.parse("1.11")






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 1e-4






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 12

