
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowercase  :					Any						    =							logging.get_logger(__name__)

lowercase  :					Optional[int]						    =							{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Any=				"deberta-v2"
    def __init__(  self		,snake_case=128100		,snake_case=1536		,snake_case=24		,snake_case=24		,snake_case=6144		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=0		,snake_case=0.02		,snake_case=1e-7		,snake_case=False		,snake_case=-1		,snake_case=0		,snake_case=True		,snake_case=None		,snake_case=0		,snake_case="gelu"		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case      )

           lowercase							:     Union[str, Any]       	=      hidden_size
           lowercase							:     int       	=      num_hidden_layers
           lowercase							:     str       	=      num_attention_heads
           lowercase							:     Any       	=      intermediate_size
           lowercase							:     Any       	=      hidden_act
           lowercase							:     Optional[Any]       	=      hidden_dropout_prob
           lowercase							:     List[Any]       	=      attention_probs_dropout_prob
           lowercase							:     Optional[Any]       	=      max_position_embeddings
           lowercase							:     Optional[int]       	=      type_vocab_size
           lowercase							:     Optional[int]       	=      initializer_range
           lowercase							:     Tuple       	=      relative_attention
           lowercase							:     Any       	=      max_relative_positions
           lowercase							:     Any       	=      pad_token_id
           lowercase							:     Optional[int]       	=      position_biased_input

           # Backwards compatibility
           if type(snake_case      ) == str:
                  lowercase							:     Tuple       	=      [x.strip() for x in pos_att_type.lower().split("""|"""      )]

           lowercase							:     List[str]       	=      pos_att_type
           lowercase							:     Dict       	=      vocab_size
           lowercase							:     int       	=      layer_norm_eps

           lowercase							:     Union[str, Any]       	=      kwargs.get("""pooler_hidden_size"""		,snake_case      )
           lowercase							:     Any       	=      pooler_dropout
           lowercase							:     Dict       	=      pooler_hidden_act






class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.task == "multiple-choice":
                  lowercase							:     Union[str, Any]       	=      {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}
           if self._config.type_vocab_size > 0:
                  return OrderedDict(
                      [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]      )
           else:
                  return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 12
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,snake_case = 3		,snake_case = 40		,snake_case = 40		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      super().generate_dummy_inputs(preprocessor=snake_case		,framework=snake_case      )
           if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                  del dummy_inputs["token_type_ids"]
           return dummy_inputs


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=0.999					,	SCREAMING_SNAKE_CASE__="cosine"					,	) ->    List[Any]:
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__				):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__				):
                     return math.exp(t * -12.0				)

       else:
              raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"				)

       lowercase							:     int       	=      []
       for i in range(SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      i / num_diffusion_timesteps
              lowercase							:     Optional[int]       	=      (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__				) / alpha_bar_fn(SCREAMING_SNAKE_CASE__				)					,	SCREAMING_SNAKE_CASE__				)				)
       return torch.tensor(SCREAMING_SNAKE_CASE__					,	dtype=torch.floataa				)




class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase     ):
    _a		:						Tuple=				[e.name for e in KarrasDiffusionSchedulers]
    _a		:						int=				2
    @register_to_config
    def __init__(  self		,snake_case = 1000		,snake_case = 0.00_085		,snake_case = 0.012		,snake_case = "linear"		,snake_case = None		,snake_case = "epsilon"		,snake_case = False		,snake_case = False		,snake_case = 1.0		,snake_case = "linspace"		,snake_case = 0		,):



           '''simple docstring'''







           if trained_betas is not None:
                  lowercase							:     List[str]       	=      torch.tensor(snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "linear":
                  lowercase							:     Optional[Any]       	=      torch.linspace(snake_case		,snake_case		,snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  lowercase							:     int       	=      (
                      torch.linspace(beta_start**0.5		,beta_end**0.5		,snake_case		,dtype=torch.floataa      ) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  lowercase							:     Union[str, Any]       	=      betas_for_alpha_bar(snake_case		,alpha_transform_type="""cosine"""      )
           elif beta_schedule == "exp":
                  lowercase							:     int       	=      betas_for_alpha_bar(snake_case		,alpha_transform_type="""exp"""      )
           else:
                  raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"      )

           lowercase							:     Any       	=      1.0 - self.betas
           lowercase							:     Dict       	=      torch.cumprod(self.alphas		,dim=0      )

           #  set all values
           self.set_timesteps(snake_case		,snake_case		,snake_case      )
           lowercase							:     Tuple       	=      use_karras_sigmas
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           if schedule_timesteps is None:
                  lowercase							:     Union[str, Any]       	=      self.timesteps

           lowercase							:     Dict       	=      (schedule_timesteps == timestep).nonzero()

           # The sigma index that is taken for the **very** first `step`
           # is always the second index (or the last index if there is only 1)
           # This way we can ensure we don't accidentally skip a sigma in
           # case we start in the middle of the denoising schedule (e.g. for image-to-image)
           if len(self._index_counter      ) == 0:
                  lowercase							:     Dict       	=      1 if len(snake_case      ) > 1 else 0
           else:
                  lowercase							:     Union[str, Any]       	=      timestep.cpu().item() if torch.is_tensor(snake_case      ) else timestep
                  lowercase							:     str       	=      self._index_counter[timestep_int]

           return indices[pos].item()
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.config.timestep_spacing in ["linspace", "trailing"]:
                  return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.index_for_timestep(snake_case      )

           lowercase							:     Dict       	=      self.sigmas[step_index]
           lowercase							:     List[str]       	=      sample / ((sigma**2 + 1) ** 0.5)
           return sample
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      num_inference_steps

           lowercase							:     List[Any]       	=      num_train_timesteps or self.config.num_train_timesteps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
                  lowercase							:     Optional[int]       	=      np.linspace(0		,num_train_timesteps - 1		,snake_case		,dtype=snake_case      )[::-1].copy()
           elif self.config.timestep_spacing == "leading":
                  lowercase							:     int       	=      num_train_timesteps // self.num_inference_steps
                  # creates integer timesteps by multiplying by ratio
                  # casting to int to avoid issues when num_inference_step is power of 3
                  lowercase							:     List[str]       	=      (np.arange(0		,snake_case      ) * step_ratio).round()[::-1].copy().astype(snake_case      )
                  timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                  lowercase							:     List[str]       	=      num_train_timesteps / self.num_inference_steps
                  # creates integer timesteps by multiplying by ratio
                  # casting to int to avoid issues when num_inference_step is power of 3
                  lowercase							:     Optional[int]       	=      (np.arange(snake_case		,0		,-step_ratio      )).round().copy().astype(snake_case      )
                  timesteps -= 1
           else:
                  raise ValueError(
                      f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

           lowercase							:     Optional[int]       	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
           lowercase							:     Dict       	=      np.log(snake_case      )
           lowercase							:     Union[str, Any]       	=      np.interp(snake_case		,np.arange(0		,len(snake_case      )      )		,snake_case      )

           if self.config.use_karras_sigmas:
                  lowercase							:     List[Any]       	=      self._convert_to_karras(in_sigmas=snake_case		,num_inference_steps=self.num_inference_steps      )
                  lowercase							:     Tuple       	=      np.array([self._sigma_to_t(snake_case		,snake_case      ) for sigma in sigmas]      )

           lowercase							:     Any       	=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
           lowercase							:     List[Any]       	=      torch.from_numpy(snake_case      ).to(device=snake_case      )
           lowercase							:     List[Any]       	=      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

           lowercase							:     Dict       	=      torch.from_numpy(snake_case      )
           lowercase							:     List[Any]       	=      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

           if str(snake_case      ).startswith("""mps"""      ):
                  # mps does not support float64
                  lowercase							:     Any       	=      timesteps.to(snake_case		,dtype=torch.floataa      )
           else:
                  lowercase							:     str       	=      timesteps.to(device=snake_case      )

           # empty dt and derivative
           lowercase							:     Union[str, Any]       	=      None
           lowercase							:     Any       	=      None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           lowercase							:     str       	=      defaultdict(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      np.log(snake_case      )

           # get distribution
           lowercase							:     Union[str, Any]       	=      log_sigma - log_sigmas[:, np.newaxis]

           # get sigmas range
           lowercase							:     Optional[int]       	=      np.cumsum((dists >= 0)		,axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
           lowercase							:     Any       	=      low_idx + 1

           lowercase							:     str       	=      log_sigmas[low_idx]
           lowercase							:     Dict       	=      log_sigmas[high_idx]

           # interpolate sigmas
           lowercase							:     int       	=      (low - log_sigma) / (low - high)
           lowercase							:     Dict       	=      np.clip(snake_case		,0		,1      )

           # transform interpolation to time range
           lowercase							:     Optional[Any]       	=      (1 - w) * low_idx + w * high_idx
           lowercase							:     Tuple       	=      t.reshape(sigma.shape      )
           return t
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     float       	=      in_sigmas[-1].item()
           lowercase							:     float       	=      in_sigmas[0].item()

           lowercase							:     Dict       	=      7.0  # 7.0 is the value used in the paper
           lowercase							:     Optional[int]       	=      np.linspace(0		,1		,snake_case      )
           lowercase							:     int       	=      sigma_min ** (1 / rho)
           lowercase							:     Any       	=      sigma_max ** (1 / rho)
           lowercase							:     Tuple       	=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self.dt is None
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = True		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.index_for_timestep(snake_case      )

           # advance index counter by 1
           lowercase							:     Optional[int]       	=      timestep.cpu().item() if torch.is_tensor(snake_case      ) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
                  lowercase							:     str       	=      self.sigmas[step_index]
                  lowercase							:     Optional[int]       	=      self.sigmas[step_index + 1]
           else:
                  # 2nd order / Heun's method
                  lowercase							:     Dict       	=      self.sigmas[step_index - 1]
                  lowercase							:     Optional[Any]       	=      self.sigmas[step_index]

           # currently only gamma=0 is supported. This usually works best anyways.
           # We can support gamma in the future but then need to scale the timestep before
           # passing it to the model which requires a change in API
           lowercase							:     Union[str, Any]       	=      0
           lowercase							:     Any       	=      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                  lowercase							:     Any       	=      sigma_hat if self.state_in_first_order else sigma_next
                  lowercase							:     int       	=      sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
                  lowercase							:     Union[str, Any]       	=      sigma_hat if self.state_in_first_order else sigma_next
                  lowercase							:     Optional[Any]       	=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                      sample / (sigma_input**2 + 1)
                  )
           elif self.config.prediction_type == "sample":
                  lowercase							:     Optional[Any]       	=      model_output
           else:
                  raise ValueError(
                      f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

           if self.config.clip_sample:
                  lowercase							:     str       	=      pred_original_sample.clamp(
                      -self.config.clip_sample_range		,self.config.clip_sample_range      )

           if self.state_in_first_order:
                  # 2. Convert to an ODE derivative for 1st order
                  lowercase							:     Optional[int]       	=      (sample - pred_original_sample) / sigma_hat
                  # 3. delta timestep
                  lowercase							:     Union[str, Any]       	=      sigma_next - sigma_hat

                  # store for 2nd order step
                  lowercase							:     Optional[int]       	=      derivative
                  lowercase							:     Union[str, Any]       	=      dt
                  lowercase							:     Union[str, Any]       	=      sample
           else:
                  # 2. 2nd order / Heun's method
                  lowercase							:     Tuple       	=      (sample - pred_original_sample) / sigma_next
                  lowercase							:     Dict       	=      (self.prev_derivative + derivative) / 2

                  # 3. take prev timestep & sample
                  lowercase							:     Tuple       	=      self.dt
                  lowercase							:     Optional[Any]       	=      self.sample

                  # free dt and derivative
                  # Note, this puts the scheduler in "first order mode"
                  lowercase							:     List[str]       	=      None
                  lowercase							:     Tuple       	=      None
                  lowercase							:     Dict       	=      None

           lowercase							:     List[Any]       	=      sample + derivative * dt

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.sigmas.to(device=original_samples.device		,dtype=original_samples.dtype      )
           if original_samples.device.type == "mps" and torch.is_floating_point(snake_case      ):
                  # mps does not support float64
                  lowercase							:     List[Any]       	=      self.timesteps.to(original_samples.device		,dtype=torch.floataa      )
                  lowercase							:     List[str]       	=      timesteps.to(original_samples.device		,dtype=torch.floataa      )
           else:
                  lowercase							:     List[str]       	=      self.timesteps.to(original_samples.device      )
                  lowercase							:     Tuple       	=      timesteps.to(original_samples.device      )

           lowercase							:     Any       	=      [self.index_for_timestep(snake_case		,snake_case      ) for t in timesteps]

           lowercase							:     int       	=      sigmas[step_indices].flatten()
           while len(sigma.shape      ) < len(original_samples.shape      ):
                  lowercase							:     Any       	=      sigma.unsqueeze(-1      )

           lowercase							:     Optional[int]       	=      original_samples + noise * sigma
           return noisy_samples
    def __len__(  self      ):



           '''simple docstring'''







           return self.config.num_train_timesteps
