




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


__lowerCAmelCase	:       Tuple     =	logging.get_logger(__name__)

__lowerCAmelCase	:       Optional[Any]     =	{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      """mobilenet_v2"""





				def __init__(   self       :  str      ,   __lowerCamelCase       :  Optional[int]=3      ,   __lowerCamelCase       :  int=2_24      ,   __lowerCamelCase       :  int=1.0      ,   __lowerCamelCase       :  List[Any]=8      ,   __lowerCamelCase       :  List[Any]=8      ,   __lowerCamelCase       :  List[str]=6      ,   __lowerCamelCase       :  Dict=32      ,   __lowerCamelCase       :  Any=True      ,   __lowerCamelCase       :  Dict=True      ,   __lowerCamelCase       :  str="relu6"      ,   __lowerCamelCase       :  Tuple=True      ,   __lowerCamelCase       :  Dict=0.8      ,   __lowerCamelCase       :  str=0.02      ,   __lowerCamelCase       :  Union[str, Any]=0.001      ,   __lowerCamelCase       :  Any=2_55      ,   **__lowerCamelCase       :  Dict      ,   )      ->  Dict:
							super().__init__(**__lowerCamelCase							)

							if depth_multiplier <= 0:
										raise ValueError("depth_multiplier must be greater than zero."							)

							a					=    num_channels
							a					=    image_size
							a					=    depth_multiplier
							a					=    depth_divisible_by
							a					=    min_depth
							a					=    expand_ratio
							a					=    output_stride
							a					=    first_layer_is_expansion
							a					=    finegrained_output
							a					=    hidden_act
							a					=    tf_padding
							a					=    classifier_dropout_prob
							a					=    initializer_range
							a					=    layer_norm_eps
							a					=    semantic_loss_ignore_index




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Dict											=      version.parse("""1.11"""     )





				@property
				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Mapping[str, Mapping[int, str]]:
							return OrderedDict([("pixel_values", {0: "batch"})]							)





				@property
				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Mapping[str, Mapping[int, str]]:
							if self.task == "image-classification":
										return OrderedDict([("logits", {0: "batch"})]							)
							else:
										return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]							)





				@property
				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  float:
							return 1e-4












import math

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




def     __magic_name__       (				A	:      jnp.ndarray,						A	:      int,						A	:      float = 1,						A	:      float = 1,						A	:      float = 1.0E4,						A	:      bool = False,						A	:      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"""
			a					=    float(embedding_dim // 2     )
			a					=    math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			a					=    min_timescale * jnp.exp(jnp.arange(A,						dtype=jnp.floataa     ) * -log_timescale_increment     )
			a					=    jnp.expand_dims(A,						1     ) * jnp.expand_dims(A,						0     )

			# scale embeddings
			a					=    scale * emb

			if flip_sin_to_cos:
						a					=    jnp.concatenate([jnp.cos(A     ), jnp.sin(A     )],						axis=1     )
			else:
						a					=    jnp.concatenate([jnp.sin(A     ), jnp.cos(A     )],						axis=1     )
			a					=    jnp.reshape(A,						[jnp.shape(A     )[0], embedding_dim]     )
			return signal




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							jnp.dtype											=      jnp.floataa





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"							)(__lowerCamelCase							)
							a					=    nn.silu(__lowerCamelCase							)
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"							)(__lowerCamelCase							)
							return temb




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							bool											=      False
				SCREAMING_SNAKE_CASE_ :							float											=      1





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							return get_sinusoidal_embeddings(
							    __lowerCamelCase      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift							)






