





"""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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0.999			, _UpperCAmelCase="cosine"			, ):






  """simple docstring"""





  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

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





class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			int        =       [e.name for e in KarrasDiffusionSchedulers]
      lowercase_				:			Union[str, Any]        =       2

      @register_to_config
      def __init__(					self ,				snake_case_ = 1_0_0_0 ,				snake_case_ = 0.0_00_85 ,				snake_case_ = 0.0_12 ,				snake_case_ = "linear" ,				snake_case_ = None ,				snake_case_ = "epsilon" ,				snake_case_ = "linspace" ,				snake_case_ = 0 ,				):

        """simple docstring"""

        if trained_betas is not None:
          A_  :	Any    						=  torch.tensor(snake_case_ ,				dtype=torch.floataa  )
        elif beta_schedule == "linear":
          A_  :	Tuple    						=  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.
          A_  :	Optional[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
          A_  :	Union[str, Any]    						=  betas_for_alpha_bar(snake_case_  )
        else:
          raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""  )

        A_  :	int    						=  1.0 - self.betas
        A_  :	Optional[int]    						=  torch.cumprod(self.alphas ,				dim=0  )

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

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_=None  ):

        """simple docstring"""

        if schedule_timesteps is None:
          A_  :	int    						=  self.timesteps

        A_  :	Optional[int]    						=  (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_  :	str    						=  1 if len(snake_case_  ) > 1 else 0
        else:
          A_  :	Union[str, Any]    						=  timestep.cpu().item() if torch.is_tensor(snake_case_  ) else timestep
          A_  :	List[str]    						=  self._index_counter[timestep_int]

        return indices[pos].item()

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

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

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				):

        """simple docstring"""

        A_  :	int    						=  self.index_for_timestep(snake_case_  )

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

        A_  :	Union[str, Any]    						=  sample / ((sigma**2 + 1) ** 0.5)
        return sample

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = None ,				):

        """simple docstring"""

        A_  :	Tuple    						=  num_inference_steps

        A_  :	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":
          A_  :	List[Any]    						=  np.linspace(0 ,				num_train_timesteps - 1 ,				snake_case_ ,				dtype=snake_case_  )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
          A_  :	Dict    						=  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_  :	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":
          A_  :	Tuple    						=  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_  :	List[Any]    						=  (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'."""  )

        A_  :	Union[str, Any]    						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        A_  :	Union[str, Any]    						=  torch.from_numpy(np.log(snake_case_  )  ).to(snake_case_  )

        A_  :	Union[str, Any]    						=  np.interp(snake_case_ ,				np.arange(0 ,				len(snake_case_  )  ) ,				snake_case_  )
        A_  :	str    						=  np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
        A_  :	str    						=  torch.from_numpy(snake_case_  ).to(device=snake_case_  )

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

        # compute interpolated sigmas
        A_  :	Union[str, Any]    						=  sigmas.log().lerp(sigmas_down.log() ,				0.5  ).exp()
        A_  :	List[Any]    						=  0.0

        # set sigmas
        A_  :	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_  :	Tuple    						=  torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2  ), sigmas_up[-1:]]  )
        A_  :	Dict    						=  torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2  ), sigmas_down[-1:]]  )

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

        A_  :	str    						=  self.sigma_to_t(snake_case_  ).to(snake_case_ ,				dtype=timesteps.dtype  )
        A_  :	int    						=  torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,				dim=-1  ).flatten()

        A_  :	List[str]    						=  torch.cat([timesteps[:1], interleaved_timesteps]  )

        A_  :	Optional[Any]    						=  None

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

      def  lowerCamelCase_      (					self ,				snake_case_  ):

        """simple docstring"""

        A_  :	str    						=  sigma.log()

        # get distribution
        A_  :	Any    						=  log_sigma - self.log_sigmas[:, None]

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

        A_  :	List[str]    						=  self.log_sigmas[low_idx]
        A_  :	List[Any]    						=  self.log_sigmas[high_idx]

        # interpolate sigmas
        A_  :	Dict    						=  (low - log_sigma) / (low - high)
        A_  :	Union[str, Any]    						=  w.clamp(0 ,				1  )

        # transform interpolation to time range
        A_  :	List[Any]    						=  (1 - w) * low_idx + w * high_idx
        A_  :	Union[str, Any]    						=  t.view(sigma.shape  )
        return t

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self.sample is None

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = True ,				):

        """simple docstring"""

        A_  :	int    						=  self.index_for_timestep(snake_case_  )

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

        if self.state_in_first_order:
          A_  :	Union[str, Any]    						=  self.sigmas[step_index]
          A_  :	Optional[Any]    						=  self.sigmas_interpol[step_index]
          A_  :	Union[str, Any]    						=  self.sigmas_up[step_index]
          A_  :	Optional[int]    						=  self.sigmas_down[step_index - 1]
        else:
          # 2nd order / KPDM2's method
          A_  :	Optional[int]    						=  self.sigmas[step_index - 1]
          A_  :	Optional[int]    						=  self.sigmas_interpol[step_index - 1]
          A_  :	Union[str, Any]    						=  self.sigmas_up[step_index - 1]
          A_  :	List[Any]    						=  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_  :	Dict    						=  0
        A_  :	Optional[int]    						=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        A_  :	List[Any]    						=  model_output.device
        A_  :	Dict    						=  randn_tensor(model_output.shape ,				dtype=model_output.dtype ,				device=snake_case_ ,				generator=snake_case_  )

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          A_  :	Tuple    						=  sigma_hat if self.state_in_first_order else sigma_interpol
          A_  :	Tuple    						=  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_  :	Optional[Any]    						=  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_  :	Dict    						=  (sample - pred_original_sample) / sigma_hat
          # 3. delta timestep
          A_  :	List[Any]    						=  sigma_interpol - sigma_hat

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

          A_  :	List[Any]    						=  self.sample
          A_  :	Tuple    						=  None

          A_  :	int    						=  sample + derivative * dt
          A_  :	str    						=  prev_sample + noise * sigma_up

        if not return_dict:
          return (prev_sample,)

        return SchedulerOutput(prev_sample=snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				):

        """simple docstring"""

        A_  :	List[str]    						=  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
          A_  :	List[Any]    						=  self.timesteps.to(original_samples.device ,				dtype=torch.floataa  )
          A_  :	Union[str, Any]    						=  timesteps.to(original_samples.device ,				dtype=torch.floataa  )
        else:
          A_  :	Any    						=  self.timesteps.to(original_samples.device  )
          A_  :	int    						=  timesteps.to(original_samples.device  )

        A_  :	List[str]    						=  [self.index_for_timestep(snake_case_ ,				snake_case_  ) for t in timesteps]

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

        A_  :	Optional[int]    						=  original_samples + noise * sigma
        return noisy_samples

      def __len__(					self  ):

        """simple docstring"""

        return self.config.num_train_timesteps






"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase_					:						Union[str, Any]      = logging.get_logger(__name__)

lowerCamelCase_					:						Optional[Any]      = {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}

class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Optional[Any]        =       """gpt2"""
      lowercase_				:			Optional[int]        =       ["""past_key_values"""]
      lowercase_				:			Dict        =       {
          """hidden_size""": """n_embd""",
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }

      def __init__(					self ,				snake_case_=5_0_2_5_7 ,				snake_case_=1_0_2_4 ,				snake_case_=7_6_8 ,				snake_case_=1_2 ,				snake_case_=1_2 ,				snake_case_=None ,				snake_case_="gelu_new" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=1E-5 ,				snake_case_=0.02 ,				snake_case_="cls_index" ,				snake_case_=True ,				snake_case_=None ,				snake_case_=True ,				snake_case_=0.1 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=5_0_2_5_6 ,				snake_case_=5_0_2_5_6 ,				snake_case_=False ,				snake_case_=False ,				**snake_case_ ,				):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  vocab_size
        A_  :	List[Any]    						=  n_positions
        A_  :	List[Any]    						=  n_embd
        A_  :	Tuple    						=  n_layer
        A_  :	List[Any]    						=  n_head
        A_  :	List[str]    						=  n_inner
        A_  :	str    						=  activation_function
        A_  :	Optional[Any]    						=  resid_pdrop
        A_  :	int    						=  embd_pdrop
        A_  :	Tuple    						=  attn_pdrop
        A_  :	str    						=  layer_norm_epsilon
        A_  :	Any    						=  initializer_range
        A_  :	Union[str, Any]    						=  summary_type
        A_  :	Optional[Any]    						=  summary_use_proj
        A_  :	Dict    						=  summary_activation
        A_  :	Optional[int]    						=  summary_first_dropout
        A_  :	Optional[int]    						=  summary_proj_to_labels
        A_  :	Any    						=  scale_attn_weights
        A_  :	Optional[Any]    						=  use_cache
        A_  :	Dict    						=  scale_attn_by_inverse_layer_idx
        A_  :	Any    						=  reorder_and_upcast_attn

        A_  :	Optional[Any]    						=  bos_token_id
        A_  :	List[Any]    						=  eos_token_id

        super().__init__(bos_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				**snake_case_  )


class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_ = "default" ,				snake_case_ = None ,				snake_case_ = False ,				):

        """simple docstring"""

        super().__init__(snake_case_ ,				task=snake_case_ ,				patching_specs=snake_case_ ,				use_past=snake_case_  )
        if not getattr(self._config ,				'pad_token_id' ,				snake_case_  ):
          # TODO: how to do that better?
          A_  :	Tuple    						=  0

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
        if self.use_past:
          self.fill_with_past_key_values_(snake_case_ ,				direction='inputs'  )
          A_  :	List[str]    						=  {0: 'batch', 1: 'past_sequence + sequence'}
        else:
          A_  :	Tuple    						=  {0: 'batch', 1: 'sequence'}

        return common_inputs

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self._config.n_layer

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self._config.n_head

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				):

        """simple docstring"""

        A_  :	int    						=  super(snake_case_ ,				self  ).generate_dummy_inputs(
            snake_case_ ,				batch_size=snake_case_ ,				seq_length=snake_case_ ,				is_pair=snake_case_ ,				framework=snake_case_  )

        # We need to order the input in the way they appears in the forward()
        A_  :	Optional[Any]    						=  OrderedDict({'input_ids': common_inputs['input_ids']}  )

        # Need to add the past_keys
        if self.use_past:
          if not is_torch_available():
            raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
          else:
            import torch

            A_						,	A_  :	List[str]    						=  common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            A_  :	Optional[Any]    						=  seqlen + 2
            A_  :	Any    						=  (
                batch,
                self.num_attention_heads,
                past_key_values_length,
                self._config.hidden_size // self.num_attention_heads,
            )
            A_  :	Any    						=  [
                (torch.zeros(snake_case_  ), torch.zeros(snake_case_  )) for _ in range(self.num_layers  )
            ]

        A_  :	List[Any]    						=  common_inputs['attention_mask']
        if self.use_past:
          A_  :	List[Any]    						=  ordered_inputs['attention_mask'].dtype
          A_  :	Any    						=  torch.cat(
              [ordered_inputs['attention_mask'], torch.ones(snake_case_ ,				snake_case_ ,				dtype=snake_case_  )] ,				dim=1  )

        return ordered_inputs

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return 1_3