






'''simple docstring'''
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


UpperCamelCase__	=     logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				torch.FloatTensor
    __lowerCamelCase:				Optional[torch.FloatTensor]						    =      None




def 						__SCREAMING_SNAKE_CASE   (					_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}"""							)

   lowercase_		:      Optional[int]          =       []
   for i in range(_UpperCamelCase							):
      lowercase_		:      Any          =       i / num_diffusion_timesteps
      lowercase_		:      Optional[int]          =       (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  (						snake_case			,   snake_case     ):
    __lowerCamelCase:				str						    =      [e.name for e in KarrasDiffusionSchedulers]
    __lowerCamelCase:				Tuple						    =      1




    @register_to_config
    def __init__(     self					:    List[Any]					,       a					:    int = 1_0_0_0					,       a					:    float = 0.0001					,       a					:    float = 0.02					,       a					:    str = "linear"					,       a					:    Optional[Union[np.ndarray, List[float]]] = None					,       a					:    str = "epsilon"					,       a					:    str = "linspace"					,       a					:    int = 0					,       ):






       '''simple docstring'''






       if trained_betas is not None:
          lowercase_		:      Optional[int]          =       torch.tensor(a					,       dtype=torch.floataa     )
       elif beta_schedule == "linear":
          lowercase_		:      Union[str, Any]          =       torch.linspace(a					,       a					,       a					,       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					,       a					,       dtype=torch.floataa     ) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_		:      Optional[int]          =       betas_for_alpha_bar(a     )
       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_		:      str          =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
       lowercase_		:      List[Any]          =       np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
       lowercase_		:      List[str]          =       torch.from_numpy(a     )

       # setable values
       lowercase_		:      Tuple          =       None
       lowercase_		:      str          =       np.linspace(0					,       num_train_timesteps - 1					,       a					,       dtype=a     )[::-1].copy()
       lowercase_		:      str          =       torch.from_numpy(a     )
       lowercase_		:      List[Any]          =       False




    @property
    def 			lowerCAmelCase__			(     self					:    str     ):






       '''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					:    Tuple					,       a					:    torch.FloatTensor					,       a					:    Union[float, torch.FloatTensor]     ):






       '''simple docstring'''






       if isinstance(a					,       torch.Tensor     ):
          lowercase_		:      Any          =       timestep.to(self.timesteps.device     )
       lowercase_		:      Any          =       (self.timesteps == timestep).nonzero().item()
       lowercase_		:      Dict          =       self.sigmas[step_index]
       lowercase_		:      str          =       sample / ((sigma**2 + 1) ** 0.5)
       lowercase_		:      Union[str, Any]          =       True
       return sample




    def 			lowerCAmelCase__			(     self					:    Dict					,       a					:    int					,       a					:    Union[str, torch.device] = None     ):






       '''simple docstring'''






       lowercase_		:      str          =       num_inference_steps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
          lowercase_		:      Union[str, Any]          =       np.linspace(0					,       self.config.num_train_timesteps - 1					,       a					,       dtype=a     )[
              ::-1
          ].copy()
       elif self.config.timestep_spacing == "leading":
          lowercase_		:      Union[str, Any]          =       self.config.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_		:      Tuple          =       (np.arange(0					,       a     ) * step_ratio).round()[::-1].copy().astype(a     )
          timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
          lowercase_		:      Optional[int]          =       self.config.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[Any]          =       (np.arange(self.config.num_train_timesteps					,       0					,       -step_ratio     )).round().copy().astype(a     )
          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_		:      List[Any]          =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
       lowercase_		:      List[str]          =       np.interp(a					,       np.arange(0					,       len(a     )     )					,       a     )
       lowercase_		:      Dict          =       np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
       lowercase_		:      Optional[int]          =       torch.from_numpy(a     ).to(device=a     )
       if str(a     ).startswith("mps"     ):
          # mps does not support float64
          lowercase_		:      List[Any]          =       torch.from_numpy(a     ).to(a					,       dtype=torch.floataa     )
       else:
          lowercase_		:      Dict          =       torch.from_numpy(a     ).to(device=a     )




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    torch.FloatTensor					,       a					:    Union[float, torch.FloatTensor]					,       a					:    torch.FloatTensor					,       a					:    Optional[torch.Generator] = None					,       a					:    bool = True					,       ):






       '''simple docstring'''






       if (
           isinstance(a					,       a     )
           or isinstance(a					,       torch.IntTensor     )
           or isinstance(a					,       torch.LongTensor     )
       ):
          raise ValueError(
              (
                  "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                  " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                  " one of the `scheduler.timesteps` as a timestep."
              )					,       )

       if not self.is_scale_input_called:
          logger.warning(
              "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
              "See `StableDiffusionPipeline` for a usage example."     )

       if isinstance(a					,       torch.Tensor     ):
          lowercase_		:      Tuple          =       timestep.to(self.timesteps.device     )

       lowercase_		:      str          =       (self.timesteps == timestep).nonzero().item()
       lowercase_		:      Any          =       self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
          lowercase_		:      List[str]          =       sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          lowercase_		:      Any          =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`"""     )

       lowercase_		:      str          =       self.sigmas[step_index]
       lowercase_		:      Union[str, Any]          =       self.sigmas[step_index + 1]
       lowercase_		:      List[Any]          =       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
       lowercase_		:      List[Any]          =       (sigma_to**2 - sigma_up**2) ** 0.5

       # 2. Convert to an ODE derivative
       lowercase_		:      Tuple          =       (sample - pred_original_sample) / sigma

       lowercase_		:      Optional[int]          =       sigma_down - sigma

       lowercase_		:      Dict          =       sample + derivative * dt

       lowercase_		:      Optional[int]          =       model_output.device
       lowercase_		:      Dict          =       randn_tensor(model_output.shape					,       dtype=model_output.dtype					,       device=a					,       generator=a     )

       lowercase_		:      List[str]          =       prev_sample + noise * sigma_up

       if not return_dict:
          return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=a					,       pred_original_sample=a     )




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    torch.FloatTensor					,       a					:    torch.FloatTensor					,       a					:    torch.FloatTensor					,       ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       self.sigmas.to(device=original_samples.device					,       dtype=original_samples.dtype     )
       if original_samples.device.type == "mps" and torch.is_floating_point(a     ):
          # mps does not support float64
          lowercase_		:      Tuple          =       self.timesteps.to(original_samples.device					,       dtype=torch.floataa     )
          lowercase_		:      List[Any]          =       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          =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       lowercase_		:      List[str]          =       original_samples + noise * sigma
       return noisy_samples





    def __len__(     self					:    Optional[int]     ):






       '''simple docstring'''






       return self.config.num_train_timesteps












'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__	=     logging.get_logger(__name__)

UpperCamelCase__	=     {
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				List[Any]						    =      'm2m_100'
    __lowerCamelCase:				Union[str, Any]						    =      ['past_key_values']
    __lowerCamelCase:				Union[str, Any]						    =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




    def __init__(     self					:    str					,       a					:    Optional[Any]=1_2_8_1_1_2					,       a					:    Optional[int]=1_0_2_4					,       a					:    Any=1_2					,       a					:    Tuple=4_0_9_6					,       a					:    Any=1_6					,       a					:    Any=1_2					,       a					:    Dict=4_0_9_6					,       a					:    List[str]=1_6					,       a					:    List[Any]=0.05					,       a					:    Optional[int]=0.05					,       a					:    List[Any]=True					,       a					:    List[Any]=True					,       a					:    Union[str, Any]="relu"					,       a					:    Optional[Any]=1_0_2_4					,       a					:    List[str]=0.1					,       a					:    Union[str, Any]=0.1					,       a					:    int=0.0					,       a					:    List[str]=0.02					,       a					:    Optional[int]=2					,       a					:    Tuple=True					,       a					:    List[str]=1					,       a					:    int=0					,       a					:    int=2					,       **a					:    Tuple					,       ):






       '''simple docstring'''






       lowercase_		:      List[str]          =       vocab_size
       lowercase_		:      Tuple          =       max_position_embeddings
       lowercase_		:      int          =       d_model
       lowercase_		:      Optional[int]          =       encoder_ffn_dim
       lowercase_		:      Any          =       encoder_layers
       lowercase_		:      Optional[Any]          =       encoder_attention_heads
       lowercase_		:      List[Any]          =       decoder_ffn_dim
       lowercase_		:      Union[str, Any]          =       decoder_layers
       lowercase_		:      Optional[Any]          =       decoder_attention_heads
       lowercase_		:      Optional[int]          =       dropout
       lowercase_		:      Optional[Any]          =       attention_dropout
       lowercase_		:      Optional[int]          =       activation_dropout
       lowercase_		:      Any          =       activation_function
       lowercase_		:      Optional[Any]          =       init_std
       lowercase_		:      Tuple          =       encoder_layerdrop
       lowercase_		:      Optional[Any]          =       decoder_layerdrop
       lowercase_		:      List[Any]          =       use_cache
       lowercase_		:      Dict          =       encoder_layers
       lowercase_		:      Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=a					,       bos_token_id=a					,       eos_token_id=a					,       is_encoder_decoder=a					,       decoder_start_token_id=a					,       **a					,       )


class     _UpperCAmelCase  (						snake_case     ):




    @property
    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]     )

       if self.use_past:
          lowercase_		:      Optional[int]          =       {0: "batch"}
          lowercase_		:      str          =       {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          lowercase_		:      Optional[Any]          =       {0: "batch", 1: "decoder_sequence"}
          lowercase_		:      Union[str, Any]          =       {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
          self.fill_with_past_key_values_(a					,       direction="inputs"     )
       return common_inputs




    def 			lowerCAmelCase__			(     self					:    Dict					,       a					:    PreTrainedTokenizer					,       a					:    int = -1					,       a					:    int = -1					,       a					:    bool = False					,       a					:    Optional[TensorType] = None					,       ):






       '''simple docstring'''






       lowercase_		:      int          =       compute_effective_axis_dimension(
           a					,       fixed_dimension=OnnxConfig.default_fixed_batch					,       num_token_to_add=0     )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       lowercase_		:      Dict          =       tokenizer.num_special_tokens_to_add(a     )
       lowercase_		:      str          =       compute_effective_axis_dimension(
           a					,       fixed_dimension=OnnxConfig.default_fixed_sequence					,       num_token_to_add=a     )

       # Generate dummy inputs according to compute batch and sequence
       lowercase_		:      Union[str, Any]          =       [" ".join([tokenizer.unk_token]     ) * seq_length] * batch_size
       lowercase_		:      Tuple          =       dict(tokenizer(a					,       return_tensors=a     )     )
       return common_inputs




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    PreTrainedTokenizer					,       a					:    int = -1					,       a					:    int = -1					,       a					:    bool = False					,       a					:    Optional[TensorType] = None					,       ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           a					,       a					,       a					,       a					,       a     )

       # Generate decoder inputs
       lowercase_		:      List[str]          =       seq_length if not self.use_past else 1
       lowercase_		:      Dict          =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           a					,       a					,       a					,       a					,       a     )
       lowercase_		:      Union[str, Any]          =       {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       lowercase_		:      Union[str, Any]          =       dict(**a					,       **a     )

       if self.use_past:
          if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."     )
          else:
             import torch
          lowercase_      ,						lowercase_		:      Union[str, Any]          =       common_inputs["input_ids"].shape
          lowercase_		:      List[Any]          =       common_inputs["decoder_input_ids"].shape[1]
          lowercase_      ,						lowercase_		:      Optional[Any]          =       self.num_attention_heads
          lowercase_		:      Any          =       (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          lowercase_		:      Optional[Any]          =       decoder_seq_length + 3
          lowercase_		:      int          =       (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )

          lowercase_		:      Dict          =       torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(a					,       a     )]					,       dim=1     )

          lowercase_		:      Tuple          =       []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          lowercase_      ,						lowercase_		:      Tuple          =       self.num_layers
          lowercase_		:      Optional[Any]          =       min(a					,       a     )
          lowercase_		:      Tuple          =       max(a					,       a     ) - min_num_layers
          lowercase_		:      Optional[int]          =       "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

          for _ in range(a     ):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(a     ),
                     torch.zeros(a     ),
                     torch.zeros(a     ),
                     torch.zeros(a     ),
                 )     )
          # TODO: test this.
          lowercase_		:      Union[str, Any]          =       encoder_shape if remaining_side_name == "encoder" else decoder_shape
          for _ in range(a					,       a     ):
             common_inputs["past_key_values"].append((torch.zeros(a     ), torch.zeros(a     ))     )
       return common_inputs





    __lowerCamelCase:				str						    =      _generate_dummy_inputs_for_default_and_seqaseq_lm




