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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class  UpperCamelCase__ ( lowercase_			):
      a__   :     torch.FloatTensor
      a__   :     torch.FloatTensor







def 						_lowercase       (     __lowerCamelCase			:					Union[str, Any]		,__lowerCamelCase			:					int=0.9_9_9		,__lowerCamelCase			:					Dict="cosine"		,)  ->   Dict:





     '''simple docstring'''





     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__lowerCamelCase			:					int			):
               return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2			) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__lowerCamelCase			:					Optional[int]			):
               return math.exp(t * -1_2.0			)

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

     UpperCamelCase__							:  List[str]								=     []
     for i in range(__lowerCamelCase			):
          UpperCamelCase__							:  Union[str, Any]								=     i / num_diffusion_timesteps
          UpperCamelCase__							:  int								=     (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__lowerCamelCase			) / alpha_bar_fn(__lowerCamelCase			)		,__lowerCamelCase			)			)
     return torch.tensor(__lowerCamelCase		,dtype=torch.floataa			)







class  UpperCamelCase__ ( lowercase_							,						lowercase_			):
      a__   :     List[str]								=						1






      @register_to_config
      def __init__(     self					:   Tuple,		__lowerCamelCase					:   int = 10_00,		__lowerCamelCase					:   float = 0.0001,		__lowerCamelCase					:   float = 0.02,		__lowerCamelCase					:   str = "linear",		__lowerCamelCase					:   float = 0.0,		__lowerCamelCase					:   Optional[np.ndarray] = None,		__lowerCamelCase					:   bool = True,		)  -> List[str]:







           if trained_betas is not None:
                UpperCamelCase__							:  Optional[Any]								=     torch.from_numpy(__lowerCamelCase   )
           elif beta_schedule == "linear":
                UpperCamelCase__							:  int								=     torch.linspace(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		dtype=torch.floataa   )
           elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                UpperCamelCase__							:  List[Any]								=     (
                    torch.linspace(beta_start**0.5,		beta_end**0.5,		__lowerCamelCase,		dtype=torch.floataa   ) ** 2
                )
           elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                UpperCamelCase__							:  int								=     betas_for_alpha_bar(__lowerCamelCase   )
           elif beta_schedule == "sigmoid":
                # GeoDiff sigmoid schedule
                UpperCamelCase__							:  Any								=     torch.linspace(-6,		6,		__lowerCamelCase   )
                UpperCamelCase__							:  Union[str, Any]								=     torch.sigmoid(__lowerCamelCase   ) * (beta_end - beta_start) + beta_start
           else:
                raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'   )

           UpperCamelCase__							:  List[Any]								=     1.0 - self.betas
           UpperCamelCase__							:  str								=     torch.cumprod(self.alphas,		dim=0   )
           UpperCamelCase__							:  Optional[int]								=     torch.tensor(1.0   )

           UpperCamelCase__							:  List[str]								=     torch.tensor(1.0   )

           # standard deviation of the initial noise distribution
           UpperCamelCase__							:  str								=     1.0

           # setable values
           UpperCamelCase__							:  Optional[Any]								=     None
           UpperCamelCase__							:  List[Any]								=     torch.from_numpy(np.arange(0,		__lowerCamelCase   )[::-1].copy()   )

           UpperCamelCase__							:  Union[str, Any]								=     eta






      def 	__lowercase(     self					:   int,		__lowerCamelCase					:   torch.FloatTensor,		__lowerCamelCase					:   Optional[int] = None   )  -> Optional[Any]:
           return sample






      def 	__lowercase(     self					:   str,		__lowerCamelCase					:   int,		__lowerCamelCase					:   int = 10,		__lowerCamelCase					:   int = 10,		__lowerCamelCase					:   Union[str, torch.device] = None,		)  -> Optional[Any]:
           UpperCamelCase__							:  int								=     min(self.config.num_train_timesteps,		__lowerCamelCase   )
           UpperCamelCase__							:  Any								=     num_inference_steps

           UpperCamelCase__							:  List[str]								=     []

           UpperCamelCase__							:  List[str]								=     {}
           for j in range(0,		num_inference_steps - jump_length,		__lowerCamelCase   ):
                UpperCamelCase__							:  str								=     jump_n_sample - 1

           UpperCamelCase__							:  str								=     num_inference_steps
           while t >= 1:
                UpperCamelCase__							:  str								=     t - 1
                timesteps.append(__lowerCamelCase   )

                if jumps.get(__lowerCamelCase,		0   ) > 0:
                     UpperCamelCase__							:  Dict								=     jumps[t] - 1
                     for _ in range(__lowerCamelCase   ):
                          UpperCamelCase__							:  Any								=     t + 1
                          timesteps.append(__lowerCamelCase   )

           UpperCamelCase__							:  Dict								=     np.array(__lowerCamelCase   ) * (self.config.num_train_timesteps // self.num_inference_steps)
           UpperCamelCase__							:  Optional[int]								=     torch.from_numpy(__lowerCamelCase   ).to(__lowerCamelCase   )






      def 	__lowercase(     self					:   str,		__lowerCamelCase					:   Optional[int]   )  -> int:
           UpperCamelCase__							:  List[str]								=     t - self.config.num_train_timesteps // self.num_inference_steps

           UpperCamelCase__							:  Optional[Any]								=     self.alphas_cumprod[t]
           UpperCamelCase__							:  List[str]								=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
           UpperCamelCase__							:  Union[str, Any]								=     1 - alpha_prod_t
           UpperCamelCase__							:  Optional[Any]								=     1 - alpha_prod_t_prev

           # For t > 0, compute predicted variance βt (see formula (6) and (7) from
           # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
           # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
           # variance to pred_sample
           # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
           # without eta.
           # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
           UpperCamelCase__							:  List[str]								=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance






      def 	__lowercase(     self					:   int,		__lowerCamelCase					:   torch.FloatTensor,		__lowerCamelCase					:   int,		__lowerCamelCase					:   torch.FloatTensor,		__lowerCamelCase					:   torch.FloatTensor,		__lowerCamelCase					:   torch.FloatTensor,		__lowerCamelCase					:   Optional[torch.Generator] = None,		__lowerCamelCase					:   bool = True,		)  -> Optional[Any]:
           UpperCamelCase__							:  Any								=     timestep
           UpperCamelCase__							:  Tuple								=     timestep - self.config.num_train_timesteps // self.num_inference_steps

           # 1. compute alphas, betas
           UpperCamelCase__							:  Optional[Any]								=     self.alphas_cumprod[t]
           UpperCamelCase__							:  List[Any]								=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
           UpperCamelCase__							:  int								=     1 - alpha_prod_t

           # 2. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
           UpperCamelCase__							:  List[Any]								=     (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                UpperCamelCase__							:  Union[str, Any]								=     torch.clamp(__lowerCamelCase,		-1,		1   )

           # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
           # substitute formula (7) in the algorithm coming from DDPM paper
           # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
           # DDIM schedule gives the same results as DDPM with eta = 1.0
           # Noise is being reused in 7. and 8., but no impact on quality has
           # been observed.

           # 5. Add noise
           UpperCamelCase__							:  Union[str, Any]								=     model_output.device
           UpperCamelCase__							:  Dict								=     randn_tensor(model_output.shape,		generator=__lowerCamelCase,		device=__lowerCamelCase,		dtype=model_output.dtype   )
           UpperCamelCase__							:  Optional[Any]								=     self.eta * self._get_variance(__lowerCamelCase   ) ** 0.5

           UpperCamelCase__							:  int								=     0
           if t > 0 and self.eta > 0:
                UpperCamelCase__							:  int								=     std_dev_t * noise

           # 6. compute "direction pointing to x_t" of formula (12)
           # from https://arxiv.org/pdf/2010.02502.pdf
           UpperCamelCase__							:  Optional[Any]								=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

           # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           UpperCamelCase__							:  Tuple								=     alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

           # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
           UpperCamelCase__							:  Optional[Any]								=     (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

           # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
           UpperCamelCase__							:  Tuple								=     mask * prev_known_part + (1.0 - mask) * prev_unknown_part

           if not return_dict:
                return (
                    pred_prev_sample,
                    pred_original_sample,
                )

           return RePaintSchedulerOutput(prev_sample=__lowerCamelCase,		pred_original_sample=__lowerCamelCase   )






      def 	__lowercase(     self					:   Tuple,		__lowerCamelCase					:   int,		__lowerCamelCase					:   Any,		__lowerCamelCase					:   List[str]=None   )  -> List[str]:
           UpperCamelCase__							:  Union[str, Any]								=     self.config.num_train_timesteps // self.num_inference_steps

           for i in range(__lowerCamelCase   ):
                UpperCamelCase__							:  int								=     self.betas[timestep + i]
                if sample.device.type == "mps":
                     # randn does not work reproducibly on mps
                     UpperCamelCase__							:  Tuple								=     randn_tensor(sample.shape,		dtype=sample.dtype,		generator=__lowerCamelCase   )
                     UpperCamelCase__							:  str								=     noise.to(sample.device   )
                else:
                     UpperCamelCase__							:  int								=     randn_tensor(sample.shape,		generator=__lowerCamelCase,		device=sample.device,		dtype=sample.dtype   )

                # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
                UpperCamelCase__							:  List[Any]								=     (1 - beta) ** 0.5 * sample + beta**0.5 * noise

           return sample






      def 	__lowercase(     self					:   str,		__lowerCamelCase					:   torch.FloatTensor,		__lowerCamelCase					:   torch.FloatTensor,		__lowerCamelCase					:   torch.IntTensor,		)  -> str:
           raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''   )






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








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

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

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """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

   _SCREAMING_SNAKE_CASE :					Any			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




