

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase_					  =   logging.get_logger(__name__)

lowerCamelCase_					  =   {
    '''shi-labs/oneformer_ade20k_swin_tiny''': (
        '''https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'''
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}







class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : Tuple  			=   'oneformer'
      lowerCamelCase_      : Any  			=   {'hidden_size': 'hidden_dim'}







      def __init__(	self				,    lowerCamelCase = None				,    lowerCamelCase = 255				,    lowerCamelCase = 150				,    lowerCamelCase = 0.1				,    lowerCamelCase = 2.0				,    lowerCamelCase = 5.0				,    lowerCamelCase = 5.0				,    lowerCamelCase = 0.5				,    lowerCamelCase = 0.07				,    lowerCamelCase = 12544				,    lowerCamelCase = 3.0				,    lowerCamelCase = 0.75				,    lowerCamelCase = 0.02				,    lowerCamelCase = 1.0				,    lowerCamelCase = 1e-05				,    lowerCamelCase = False				,    lowerCamelCase = True				,    lowerCamelCase = True				,    lowerCamelCase = [4, 8, 16, 32]				,    lowerCamelCase = 77				,    lowerCamelCase = 256				,    lowerCamelCase = 77				,    lowerCamelCase = 6				,    lowerCamelCase = 49408				,    lowerCamelCase = 2				,    lowerCamelCase = 16				,    lowerCamelCase = 256				,    lowerCamelCase = 256				,    lowerCamelCase = 256				,    lowerCamelCase = 1024				,    lowerCamelCase = "GN"				,    lowerCamelCase = 6				,    lowerCamelCase = 10				,    lowerCamelCase = True				,    lowerCamelCase = 8				,    lowerCamelCase = 0.1				,    lowerCamelCase = 2048				,    lowerCamelCase = False				,    lowerCamelCase = False				,    lowerCamelCase = 2				,    lowerCamelCase = 4				,    **lowerCamelCase				,    )		->			Optional[Any]:
        if backbone_config is None:
          logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""  )
          snake_case_					= CONFIG_MAPPING["""swin"""](
              image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=lowerCamelCase				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
        elif isinstance(lowerCamelCase				,    lowerCamelCase  ):
          snake_case_					= backbone_config.get("""model_type"""  )
          snake_case_					= CONFIG_MAPPING[backbone_model_type]
          snake_case_					= config_class.from_dict(lowerCamelCase  )

        snake_case_					= backbone_config

        snake_case_					= ignore_value
        snake_case_					= num_queries
        snake_case_					= no_object_weight
        snake_case_					= class_weight
        snake_case_					= mask_weight
        snake_case_					= dice_weight
        snake_case_					= contrastive_weight
        snake_case_					= contrastive_temperature
        snake_case_					= train_num_points
        snake_case_					= oversample_ratio
        snake_case_					= importance_sample_ratio
        snake_case_					= init_std
        snake_case_					= init_xavier_std
        snake_case_					= layer_norm_eps
        snake_case_					= is_training
        snake_case_					= use_auxiliary_loss
        snake_case_					= output_auxiliary_logits
        snake_case_					= strides
        snake_case_					= task_seq_len
        snake_case_					= text_encoder_width
        snake_case_					= text_encoder_context_length
        snake_case_					= text_encoder_num_layers
        snake_case_					= text_encoder_vocab_size
        snake_case_					= text_encoder_proj_layers
        snake_case_					= text_encoder_n_ctx
        snake_case_					= conv_dim
        snake_case_					= mask_dim
        snake_case_					= hidden_dim
        snake_case_					= encoder_feedforward_dim
        snake_case_					= norm
        snake_case_					= encoder_layers
        snake_case_					= decoder_layers
        snake_case_					= use_task_norm
        snake_case_					= num_attention_heads
        snake_case_					= dropout
        snake_case_					= dim_feedforward
        snake_case_					= pre_norm
        snake_case_					= enforce_input_proj
        snake_case_					= query_dec_layers
        snake_case_					= common_stride
        snake_case_					= decoder_layers

        super().__init__(**lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Dict[str, any]:
        snake_case_					= copy.deepcopy(self.__dict__  )
        snake_case_					= self.backbone_config.to_dict()
        snake_case_					= self.__class__.model_type
        return output


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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class 					__lowerCamelCase							:
      lowerCamelCase_      : CommonSchedulerState

      # setable values
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : Optional[int]  			=   None

      # running values
      lowerCamelCase_      : Optional[jnp.ndarray]  			=   None







      @classmethod
      def       lowerCAmelCase_       (	cls				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Union[str, Any]:
        return cls(common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase  )







@dataclass
class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : LMSDiscreteSchedulerState







class 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : str  			=   [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCamelCase_      : jnp.dtype







      @property
      def       lowerCAmelCase_       (	self  )		->			List[str]:
        return True







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = None				,    lowerCamelCase = "epsilon"				,    lowerCamelCase = jnp.floataa				,    )		->			Optional[Any]:
        snake_case_					= dtype







      def       lowerCAmelCase_       (	self				,    lowerCamelCase = None  )		->			LMSDiscreteSchedulerState:
        if common is None:
          snake_case_					= CommonSchedulerState.create(self  )

        snake_case_					= jnp.arange(0				,    self.config.num_train_timesteps  ).round()[::-1]
        snake_case_					= ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case_					= sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			jnp.ndarray:
        ((snake_case_)		,							)					= jnp.where(state.timesteps == timestep				,    size=1  )
        snake_case_					= step_index[0]

        snake_case_					= state.sigmas[step_index]
        snake_case_					= sample / ((sigma**2 + 1) ** 0.5)
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:

        def lms_derivative(lowerCamelCase  ):
          snake_case_					= 1.0
          for k in range(lowerCamelCase  ):
            if current_order == k:
              continue
            prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
          return prod

        snake_case_					= integrate.quad(lowerCamelCase				,    state.sigmas[t]				,    state.sigmas[t + 1]				,    epsrel=1e-4  )[0]

        return integrated_coeff







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = ()  )		->			LMSDiscreteSchedulerState:
        snake_case_					= jnp.linspace(self.config.num_train_timesteps - 1				,    0				,    lowerCamelCase				,    dtype=self.dtype  )

        snake_case_					= jnp.floor(lowerCamelCase  ).astype(jnp.intaa  )
        snake_case_					= jnp.ceil(lowerCamelCase  ).astype(jnp.intaa  )

        snake_case_					= jnp.mod(lowerCamelCase				,    1.0  )

        snake_case_					= ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case_					= (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case_					= jnp.concatenate([sigmas, jnp.array([0.0]				,    dtype=self.dtype  )]  )

        snake_case_					= timesteps.astype(jnp.intaa  )

        # initial running values
        snake_case_					= jnp.zeros((0,) + shape				,    dtype=self.dtype  )

        return state.replace(
            timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    num_inference_steps=lowerCamelCase				,    derivatives=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = 4				,    lowerCamelCase = True				,    )		->			Union[FlaxLMSSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

        snake_case_					= state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          snake_case_					= sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          snake_case_					= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

        # 2. Convert to an ODE derivative
        snake_case_					= (sample - pred_original_sample) / sigma
        snake_case_					= state.replace(derivatives=jnp.append(state.derivatives				,    lowerCamelCase  )  )
        if len(state.derivatives  ) > order:
          snake_case_					= state.replace(derivatives=jnp.delete(state.derivatives				,    0  )  )

        # 3. Compute linear multistep coefficients
        snake_case_					= min(timestep + 1				,    lowerCamelCase  )
        snake_case_					= [self.get_lms_coefficient(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  ) for curr_order in range(lowerCamelCase  )]

        # 4. Compute previous sample based on the derivatives path
        snake_case_					= sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCamelCase				,    reversed(state.derivatives  )  )  )

        if not return_dict:
          return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase				,    state=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			jnp.ndarray:
        snake_case_					= state.sigmas[timesteps].flatten()
        snake_case_					= broadcast_to_shape_from_left(lowerCamelCase				,    noise.shape  )

        snake_case_					= original_samples + noise * sigma

        return noisy_samples







      def __len__(	self  )		->			Optional[Any]:
        return self.config.num_train_timesteps