
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 __snake_case	(   lowerCAmelCase  ):
    _a	:		torch.FloatTensor
    _a	:		torch.FloatTensor







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=0.999      ,			SCREAMING_SNAKE_CASE__="cosine"      ,			)				->					Tuple:
       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							:  Union[str, Any] 			=   i / num_diffusion_timesteps
              lowercase							:  List[str] 			=   (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	:		List[Any]=       1
    @register_to_config
    def __init__(						self	,snake_case = 1000	,snake_case = 0.0_001	,snake_case = 0.02	,snake_case = "linear"	,snake_case = 0.0	,snake_case = None	,snake_case = True	,):



           '''simple docstring'''







           if trained_betas is not None:
                  lowercase							:  Union[str, Any] 			=   torch.from_numpy(snake_case   )
           elif beta_schedule == "linear":
                  lowercase							:  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							:  Union[str, Any] 			=   (
                      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							:  int 			=   betas_for_alpha_bar(snake_case   )
           elif beta_schedule == "sigmoid":
                  # GeoDiff sigmoid schedule
                  lowercase							:  Optional[Any] 			=   torch.linspace(-6	,6	,snake_case   )
                  lowercase							:  Union[str, Any] 			=   torch.sigmoid(snake_case   ) * (beta_end - beta_start) + beta_start
           else:
                  raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"   )

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

           lowercase							:  Any 			=   torch.tensor(1.0   )

           # standard deviation of the initial noise distribution
           lowercase							:  Dict 			=   1.0

           # setable values
           lowercase							:  Optional[int] 			=   None
           lowercase							:  Optional[Any] 			=   torch.from_numpy(np.arange(0	,snake_case   )[::-1].copy()   )

           lowercase							:  List[str] 			=   eta
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = None   ):



           '''simple docstring'''







           return sample
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = 10	,snake_case = 10	,snake_case = None	,):



           '''simple docstring'''







           lowercase							:  Tuple 			=   min(self.config.num_train_timesteps	,snake_case   )
           lowercase							:  int 			=   num_inference_steps

           lowercase							:  int 			=   []

           lowercase							:  Optional[Any] 			=   {}
           for j in range(0	,num_inference_steps - jump_length	,snake_case   ):
                  lowercase							:  int 			=   jump_n_sample - 1

           lowercase							:  List[Any] 			=   num_inference_steps
           while t >= 1:
                  lowercase							:  Union[str, Any] 			=   t - 1
                  timesteps.append(snake_case   )

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

           lowercase							:  Tuple 			=   np.array(snake_case   ) * (self.config.num_train_timesteps // self.num_inference_steps)
           lowercase							:  str 			=   torch.from_numpy(snake_case   ).to(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  str 			=   t - self.config.num_train_timesteps // self.num_inference_steps

           lowercase							:  Tuple 			=   self.alphas_cumprod[t]
           lowercase							:  List[str] 			=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
           lowercase							:  Tuple 			=   1 - alpha_prod_t
           lowercase							:  Optional[int] 			=   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]
           lowercase							:  Optional[int] 			=   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case = None	,snake_case = True	,):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   timestep
           lowercase							:  int 			=   timestep - self.config.num_train_timesteps // self.num_inference_steps

           # 1. compute alphas, betas
           lowercase							:  int 			=   self.alphas_cumprod[t]
           lowercase							:  Any 			=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
           lowercase							:  List[str] 			=   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
           lowercase							:  Tuple 			=   (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                  lowercase							:  Optional[Any] 			=   torch.clamp(snake_case	,-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
           lowercase							:  Union[str, Any] 			=   model_output.device
           lowercase							:  List[str] 			=   randn_tensor(model_output.shape	,generator=snake_case	,device=snake_case	,dtype=model_output.dtype   )
           lowercase							:  Optional[int] 			=   self.eta * self._get_variance(snake_case   ) ** 0.5

           lowercase							:  Optional[Any] 			=   0
           if t > 0 and self.eta > 0:
                  lowercase							:  Optional[Any] 			=   std_dev_t * noise

           # 6. compute "direction pointing to x_t" of formula (12)
           # from https://arxiv.org/pdf/2010.02502.pdf
           lowercase							:  List[str] 			=   (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
           lowercase							:  Dict 			=   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
           lowercase							:  str 			=   (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
           lowercase							:  str 			=   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=snake_case	,pred_original_sample=snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case=None   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.config.num_train_timesteps // self.num_inference_steps

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

                  # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
                  lowercase							:  str 			=   (1 - beta) ** 0.5 * sample + beta**0.5 * noise

           return sample
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case	,):



           '''simple docstring'''







           raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""   )
    def __len__(						self   ):



           '''simple docstring'''







           return self.config.num_train_timesteps


from typing import TYPE_CHECKING

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


lowercase						:      Any							=							{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[Any]							=							["""GPT2TokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							[
      """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPT2DoubleHeadsModel""",
      """GPT2ForQuestionAnswering""",
      """GPT2ForSequenceClassification""",
      """GPT2ForTokenClassification""",
      """GPT2LMHeadModel""",
      """GPT2Model""",
      """GPT2PreTrainedModel""",
      """load_tf_weights_in_gpt2""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      Tuple							=							[
      """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFGPT2DoubleHeadsModel""",
      """TFGPT2ForSequenceClassification""",
      """TFGPT2LMHeadModel""",
      """TFGPT2MainLayer""",
      """TFGPT2Model""",
      """TFGPT2PreTrainedModel""",
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[str]							=							["""TFGPT2Tokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

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