





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class 					__A    :

			'''simple docstring'''







			lowerCAmelCase_							     =						None
			lowerCAmelCase_							     =						None
			lowerCAmelCase_							     =						None  # sigma(t_i)





			@classmethod
			def 		__lowerCamelCase				(    cls							):



				'''simple docstring'''







				return cls()






@dataclass
class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						42
			lowerCAmelCase_							     =						42
			lowerCAmelCase_							     =						42






class 					__A    (    lowerCAmelCase			,				lowerCAmelCase ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return True





			@register_to_config
			def __init__(    self       ,  __lowerCAmelCase = 0.02       ,  __lowerCAmelCase = 1_0_0       ,  __lowerCAmelCase = 1.007       ,  __lowerCAmelCase = 8_0       ,  __lowerCAmelCase = 0.05       ,  __lowerCAmelCase = 5_0       ,  ):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return KarrasVeSchedulerState.create()





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = ()							):



				'''simple docstring'''







				lowerCamelCase__								=		jnp.arange(0       ,  __lowerCAmelCase							)[::-1].copy()
				lowerCamelCase__								=		[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=__lowerCAmelCase       ,  schedule=jnp.array(__lowerCAmelCase       ,  dtype=jnp.floataa							)       ,  timesteps=__lowerCAmelCase       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				if self.config.s_min <= sigma <= self.config.s_max:
					lowerCamelCase__								=		min(self.config.s_churn / state.num_inference_steps       ,  2**0.5 - 1							)
				else:
					lowerCamelCase__								=		0

				# sample eps ~ N(0, S_noise^2 * I)
				lowerCamelCase__								=		random.split(__lowerCAmelCase       ,  num=1							)
				lowerCamelCase__								=		self.config.s_noise * random.normal(key=__lowerCAmelCase       ,  shape=sample.shape							)
				lowerCamelCase__								=		sigma + gamma * sigma
				lowerCamelCase__								=		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = True       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		sample_hat + sigma_hat * model_output
				lowerCamelCase__								=		(sample_hat - pred_original_sample) / sigma_hat
				lowerCamelCase__								=		sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
					return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=__lowerCAmelCase       ,  derivative=__lowerCAmelCase       ,  state=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = True       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		sample_prev + sigma_prev * model_output
				lowerCamelCase__								=		(sample_prev - pred_original_sample) / sigma_prev
				lowerCamelCase__								=		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
					return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=__lowerCAmelCase       ,  derivative=__lowerCAmelCase       ,  state=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				raise NotImplementedError()










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


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "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 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""yolos"""





			def __init__(    self       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=[5_1_2, 8_6_4]       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=1_0_0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.1       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(**__lowerCAmelCase							)

				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		layer_norm_eps
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		patch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		qkv_bias
				lowerCamelCase__								=		num_detection_tokens
				lowerCamelCase__								=		use_mid_position_embeddings
				lowerCamelCase__								=		auxiliary_loss
				# Hungarian matcher
				lowerCamelCase__								=		class_cost
				lowerCamelCase__								=		bbox_cost
				lowerCamelCase__								=		giou_cost
				# Loss coefficients
				lowerCamelCase__								=		bbox_loss_coefficient
				lowerCamelCase__								=		giou_loss_coefficient
				lowerCamelCase__								=		eos_coefficient






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						version.parse("""1.11""" )





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1E-4





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1_2



