





"""simple docstring"""



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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 	__lowercase  (     snake_case_						:						List[str]       ,snake_case_						:						List[str]=0.999       ,snake_case_						:						Dict="cosine"       ,)     ->Dict:



    '''simple docstring'''







    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    __A					:		Dict						=  []
    for i in range(snake_case_		):
        __A					:		List[str]						=  i / num_diffusion_timesteps
        __A					:		Optional[Any]						=  (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(snake_case_		) / alpha_bar_fn(snake_case_		)       ,snake_case_		)		)
    return torch.tensor(snake_case_       ,dtype=torch.floataa		)






class 	__snake_case      (	SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 _lowerCamelCase				 =  [e.name for e in KarrasDiffusionSchedulers]
 _lowerCamelCase				 =  2

 @register_to_config
 def __init__(					self      ,		__lowerCamelCase = 1000      ,		__lowerCamelCase = 0.0_0_0_8_5      ,		__lowerCamelCase = 0.0_1_2      ,		__lowerCamelCase = "linear"      ,		__lowerCamelCase = None      ,		__lowerCamelCase = "epsilon"      ,		__lowerCamelCase = "linspace"      ,		__lowerCamelCase = 0      ,		):



     '''simple docstring'''




     if trained_betas is not None:
         __A					:		str						=  torch.tensor(__lowerCamelCase      ,		dtype=torch.floataa						)
     elif beta_schedule == "linear":
         __A					:		Tuple						=  torch.linspace(__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		dtype=torch.floataa						)
     elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         __A					:		Tuple						=  (
             torch.linspace(beta_start**0.5      ,		beta_end**0.5      ,		__lowerCamelCase      ,		dtype=torch.floataa						) ** 2
         )
     elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         __A					:		Tuple						=  betas_for_alpha_bar(__lowerCamelCase						)
     else:
         raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""						)

     __A					:		List[str]						=  1.0 - self.betas
     __A					:		str						=  torch.cumprod(self.alphas      ,		dim=0						)

     #  set all values
     self.set_timesteps(__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase=None						):



     '''simple docstring'''




     if schedule_timesteps is None:
         __A					:		List[Any]						=  self.timesteps

     __A					:		List[str]						=  (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:
         __A					:		List[str]						=  1 if len(__lowerCamelCase						) > 1 else 0
     else:
         __A					:		str						=  timestep.cpu().item() if torch.is_tensor(__lowerCamelCase						) else timestep
         __A					:		Optional[Any]						=  self._index_counter[timestep_int]

     return indices[pos].item()

 @property
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




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

     return (self.sigmas.max() ** 2 + 1) ** 0.5

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



     '''simple docstring'''




     __A					:		Union[str, Any]						=  self.index_for_timestep(__lowerCamelCase						)

     if self.state_in_first_order:
         __A					:		Optional[Any]						=  self.sigmas[step_index]
     else:
         __A					:		Union[str, Any]						=  self.sigmas_interpol[step_index - 1]

     __A					:		Dict						=  sample / ((sigma**2 + 1) ** 0.5)
     return sample

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		):



     '''simple docstring'''




     __A					:		str						=  num_inference_steps

     __A					:		Optional[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":
         __A					:		List[Any]						=  np.linspace(0      ,		num_train_timesteps - 1      ,		__lowerCamelCase      ,		dtype=__lowerCamelCase						)[::-1].copy()
     elif self.config.timestep_spacing == "leading":
         __A					:		Union[str, Any]						=  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
         __A					:		Tuple						=  (np.arange(0      ,		__lowerCamelCase						) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase						)
         timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
         __A					:		Optional[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
         __A					:		str						=  (np.arange(__lowerCamelCase      ,		0      ,		-step_ratio						)).round().copy().astype(__lowerCamelCase						)
         timesteps -= 1
     else:
         raise ValueError(
             F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""						)

     __A					:		List[Any]						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
     __A					:		Optional[int]						=  torch.from_numpy(np.log(__lowerCamelCase						)						).to(__lowerCamelCase						)

     __A					:		Tuple						=  np.interp(__lowerCamelCase      ,		np.arange(0      ,		len(__lowerCamelCase						)						)      ,		__lowerCamelCase						)
     __A					:		Tuple						=  np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
     __A					:		Tuple						=  torch.from_numpy(__lowerCamelCase						).to(device=__lowerCamelCase						)

     # compute up and down sigmas
     __A					:		int						=  sigmas.roll(-1						)
     __A					:		List[Any]						=  0.0
     __A					:		List[str]						=  (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
     __A					:		Optional[int]						=  (sigmas_next**2 - sigmas_up**2) ** 0.5
     __A					:		str						=  0.0

     # compute interpolated sigmas
     __A					:		Dict						=  sigmas.log().lerp(sigmas_down.log()      ,		0.5						).exp()
     __A					:		Any						=  0.0

     # set sigmas
     __A					:		Optional[Any]						=  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
     __A					:		str						=  torch.cat(
         [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)
     __A					:		int						=  torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2						), sigmas_up[-1:]]						)
     __A					:		Any						=  torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2						), sigmas_down[-1:]]						)

     if str(__lowerCamelCase						).startswith('''mps'''						):
         # mps does not support float64
         __A					:		List[str]						=  torch.from_numpy(__lowerCamelCase						).to(__lowerCamelCase      ,		dtype=torch.floataa						)
     else:
         __A					:		List[Any]						=  torch.from_numpy(__lowerCamelCase						).to(__lowerCamelCase						)

     __A					:		Union[str, Any]						=  self.sigma_to_t(__lowerCamelCase						).to(__lowerCamelCase      ,		dtype=timesteps.dtype						)
     __A					:		List[str]						=  torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,		dim=-1						).flatten()

     __A					:		Union[str, Any]						=  torch.cat([timesteps[:1], interleaved_timesteps]						)

     __A					:		Any						=  None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     __A					:		int						=  defaultdict(__lowerCamelCase						)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		Any						=  sigma.log()

     # get distribution
     __A					:		str						=  log_sigma - self.log_sigmas[:, None]

     # get sigmas range
     __A					:		Any						=  dists.ge(0						).cumsum(dim=0						).argmax(dim=0						).clamp(max=self.log_sigmas.shape[0] - 2						)
     __A					:		int						=  low_idx + 1

     __A					:		Dict						=  self.log_sigmas[low_idx]
     __A					:		Optional[int]						=  self.log_sigmas[high_idx]

     # interpolate sigmas
     __A					:		Optional[Any]						=  (low - log_sigma) / (low - high)
     __A					:		Tuple						=  w.clamp(0      ,		1						)

     # transform interpolation to time range
     __A					:		Optional[int]						=  (1 - w) * low_idx + w * high_idx
     __A					:		Optional[int]						=  t.view(sigma.shape						)
     return t

 @property
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     return self.sample is None

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = None      ,		__lowerCamelCase = True      ,		):



     '''simple docstring'''




     __A					:		Any						=  self.index_for_timestep(__lowerCamelCase						)

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

     if self.state_in_first_order:
         __A					:		List[Any]						=  self.sigmas[step_index]
         __A					:		Any						=  self.sigmas_interpol[step_index]
         __A					:		Union[str, Any]						=  self.sigmas_up[step_index]
         __A					:		Optional[Any]						=  self.sigmas_down[step_index - 1]
     else:
         # 2nd order / KPDM2's method
         __A					:		Optional[Any]						=  self.sigmas[step_index - 1]
         __A					:		Any						=  self.sigmas_interpol[step_index - 1]
         __A					:		Any						=  self.sigmas_up[step_index - 1]
         __A					:		int						=  self.sigmas_down[step_index - 1]

     # 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
     __A					:		str						=  0
     __A					:		int						=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

     __A					:		str						=  model_output.device
     __A					:		List[str]						=  randn_tensor(model_output.shape      ,		dtype=model_output.dtype      ,		device=__lowerCamelCase      ,		generator=__lowerCamelCase						)

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
         __A					:		List[Any]						=  sigma_hat if self.state_in_first_order else sigma_interpol
         __A					:		List[Any]						=  sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
         __A					:		Dict						=  sigma_hat if self.state_in_first_order else sigma_interpol
         __A					:		Tuple						=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
     elif self.config.prediction_type == "sample":
         raise NotImplementedError('''prediction_type not implemented yet: sample'''						)
     else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""						)

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

         # store for 2nd order step
         __A					:		Optional[int]						=  sample
         __A					:		List[str]						=  dt
         __A					:		List[Any]						=  sample + derivative * dt
     else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         __A					:		Optional[int]						=  (sample - pred_original_sample) / sigma_interpol
         # 3. delta timestep
         __A					:		List[str]						=  sigma_down - sigma_hat

         __A					:		Union[str, Any]						=  self.sample
         __A					:		Union[str, Any]						=  None

         __A					:		Union[str, Any]						=  sample + derivative * dt
         __A					:		str						=  prev_sample + noise * sigma_up

     if not return_dict:
         return (prev_sample,)

     return SchedulerOutput(prev_sample=__lowerCamelCase						)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



     '''simple docstring'''




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

     __A					:		int						=  [self.index_for_timestep(__lowerCamelCase      ,		__lowerCamelCase						) for t in timesteps]

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

     __A					:		Dict						=  original_samples + noise * sigma
     return noisy_samples







 def __len__(					self						):



     '''simple docstring'''




     return self.config.num_train_timesteps











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_				=    {
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    ["""LayoutLMTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """LayoutLMForMaskedLM""",
           """LayoutLMForSequenceClassification""",
           """LayoutLMForTokenClassification""",
           """LayoutLMForQuestionAnswering""",
           """LayoutLMModel""",
           """LayoutLMPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFLayoutLMForMaskedLM""",
           """TFLayoutLMForSequenceClassification""",
           """TFLayoutLMForTokenClassification""",
           """TFLayoutLMForQuestionAnswering""",
           """TFLayoutLMMainLayer""",
           """TFLayoutLMModel""",
           """TFLayoutLMPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
       from .tokenization_layoutlm import LayoutLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlm import (
                  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMForMaskedLM,
                  LayoutLMForQuestionAnswering,
                  LayoutLMForSequenceClassification,
                  LayoutLMForTokenClassification,
                  LayoutLMModel,
                  LayoutLMPreTrainedModel,
              )
       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlm import (
                  TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMForMaskedLM,
                  TFLayoutLMForQuestionAnswering,
                  TFLayoutLMForSequenceClassification,
                  TFLayoutLMForTokenClassification,
                  TFLayoutLMMainLayer,
                  TFLayoutLMModel,
                  TFLayoutLMPreTrainedModel,
              )

else:
       import sys

       a_				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




