
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}
class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						="dinat"

 SCREAMING_SNAKE_CASE_				:					List[str]       						={
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }

 def __init__(						self			:			str					, SCREAMING_SNAKE_CASE__			:			str=4					, SCREAMING_SNAKE_CASE__			:			Tuple=3					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=64					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=[3, 4, 6, 5]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=[2, 4, 8, 16]					, SCREAMING_SNAKE_CASE__			:			str=7					, SCREAMING_SNAKE_CASE__			:			List[Any]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]					, SCREAMING_SNAKE_CASE__			:			int=3.0					, SCREAMING_SNAKE_CASE__			:			Dict=True					, SCREAMING_SNAKE_CASE__			:			List[Any]=0.0					, SCREAMING_SNAKE_CASE__			:			List[Any]=0.0					, SCREAMING_SNAKE_CASE__			:			str=0.1					, SCREAMING_SNAKE_CASE__			:			List[str]="gelu"					, SCREAMING_SNAKE_CASE__			:			Dict=0.02					, SCREAMING_SNAKE_CASE__			:			List[str]=1e-5					, SCREAMING_SNAKE_CASE__			:			int=0.0					, SCREAMING_SNAKE_CASE__			:			Optional[int]=None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=None					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					patch_size
      UpperCamelCase						    =					num_channels
      UpperCamelCase						    =					embed_dim
      UpperCamelCase						    =					depths
      UpperCamelCase						    =					len(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					num_heads
      UpperCamelCase						    =					kernel_size
      UpperCamelCase						    =					dilations
      UpperCamelCase						    =					mlp_ratio
      UpperCamelCase						    =					qkv_bias
      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					drop_path_rate
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					layer_norm_eps
      UpperCamelCase						    =					initializer_range
      # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      UpperCamelCase						    =					int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__   ) - 1)   )
      UpperCamelCase						    =					layer_scale_init_value
      UpperCamelCase						    =					['stem'] + [F'stage{idx}' for idx in range(1					, len(SCREAMING_SNAKE_CASE__   ) + 1   )]
      UpperCamelCase    ,		UpperCamelCase						    =					get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE__					, out_indices=SCREAMING_SNAKE_CASE__					, stage_names=self.stage_names   )




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       _lowerCAmelCase  :

 """simple docstring"""
 # setable values
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None  # sigma(t_i)

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Optional[Any]   ):






      """simple docstring"""



      return cls()




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					KarrasVeSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return KarrasVeSchedulerState.create()

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.arange(0					, SCREAMING_SNAKE_CASE__   )[::-1].copy()
      UpperCamelCase						    =					[
          (
              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=SCREAMING_SNAKE_CASE__					, schedule=jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			random.KeyArray					, ):






      """simple docstring"""



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

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

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



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

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

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

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					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=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      raise NotImplementedError()


