


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


snake_case      :   Union[str, Any]							= logging.get_logger(__name__)

snake_case      :   Tuple							= {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}



class  __lowercase	(							lowercase__				):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE :       Dict	   =    "beit"





							def __init__(     self					,			A_=8192					,			A_=768					,			A_=12					,			A_=12					,			A_=3072					,			A_="gelu"					,			A_=0.0					,			A_=0.0					,			A_=0.02					,			A_=1e-1_2					,			A_=224					,			A_=16					,			A_=3					,			A_=False					,			A_=False					,			A_=False					,			A_=False					,			A_=0.1					,			A_=0.1					,			A_=True					,			A_=[3, 5, 7, 11]					,			A_=[1, 2, 3, 6]					,			A_=True					,			A_=0.4					,			A_=256					,			A_=1					,			A_=False					,			A_=255					,			**A_					,			)->						Optional[int]:
											super().__init__(**UpperCAmelCase__						)

											_SCREAMING_SNAKE_CASE   =			vocab_size
											_SCREAMING_SNAKE_CASE   =			hidden_size
											_SCREAMING_SNAKE_CASE   =			num_hidden_layers
											_SCREAMING_SNAKE_CASE   =			num_attention_heads
											_SCREAMING_SNAKE_CASE   =			intermediate_size
											_SCREAMING_SNAKE_CASE   =			hidden_act
											_SCREAMING_SNAKE_CASE   =			hidden_dropout_prob
											_SCREAMING_SNAKE_CASE   =			attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE   =			initializer_range
											_SCREAMING_SNAKE_CASE   =			layer_norm_eps

											_SCREAMING_SNAKE_CASE   =			image_size
											_SCREAMING_SNAKE_CASE   =			patch_size
											_SCREAMING_SNAKE_CASE   =			num_channels
											_SCREAMING_SNAKE_CASE   =			use_mask_token
											_SCREAMING_SNAKE_CASE   =			use_absolute_position_embeddings
											_SCREAMING_SNAKE_CASE   =			use_relative_position_bias
											_SCREAMING_SNAKE_CASE   =			use_shared_relative_position_bias
											_SCREAMING_SNAKE_CASE   =			layer_scale_init_value
											_SCREAMING_SNAKE_CASE   =			drop_path_rate
											_SCREAMING_SNAKE_CASE   =			use_mean_pooling
											# decode head attributes (semantic segmentation)
											_SCREAMING_SNAKE_CASE   =			out_indices
											_SCREAMING_SNAKE_CASE   =			pool_scales
											# auxiliary head attributes (semantic segmentation)
											_SCREAMING_SNAKE_CASE   =			use_auxiliary_head
											_SCREAMING_SNAKE_CASE   =			auxiliary_loss_weight
											_SCREAMING_SNAKE_CASE   =			auxiliary_channels
											_SCREAMING_SNAKE_CASE   =			auxiliary_num_convs
											_SCREAMING_SNAKE_CASE   =			auxiliary_concat_input
											_SCREAMING_SNAKE_CASE   =			semantic_loss_ignore_index







class  __lowercase	(							lowercase__				):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE :       Optional[Any]	   =    version.parse("1.11"				)





							@property
							def 		__magic_name__      (     self						)->						Tuple:
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]						)





							@property
							def 		__magic_name__      (     self						)->						str:
											return 1e-4



'''simple docstring'''





import math

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






def 				_lowerCAmelCase			(   __magic_name__						:     jnp.ndarray   , __magic_name__						:     int   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1.0E4   , __magic_name__						:     bool = False   , __magic_name__						:     float = 1.0   , )				->  jnp.ndarray:
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
						lowercase :							int    		=float(embedding_dim // 2						)
						lowercase :							Optional[int]    		=math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						lowercase :							Any    		=min_timescale * jnp.exp(jnp.arange(__magic_name__   , dtype=jnp.floataa						) * -log_timescale_increment						)
						lowercase :							List[Any]    		=jnp.expand_dims(__magic_name__   , 1						) * jnp.expand_dims(__magic_name__   , 0						)

						# scale embeddings
						lowercase :							Tuple    		=scale * emb

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





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    jnp.floataa







				@nn.compact
				def __call__(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_1'''   )(UpperCAmelCase__   )
										lowercase :							Any    		=nn.silu(UpperCAmelCase__   )
										lowercase :							int    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_2'''   )(UpperCAmelCase__   )
										return temb





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    1







				@nn.compact
				def __call__(       self     :		int		,					UpperCAmelCase__     :		str   ):
										'''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   )





