





"""simple docstring"""
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 SchedulerMixin


__UpperCamelCase			:		Optional[Any]		    =   logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 	a				(					a__   ):
 snake_case__    	=    42







class 	a				(					a__					,      a__   ):
 snake_case__    	=    1



 @register_to_config
 def __init__(			self							,    _snake_case = 40							,    _snake_case = 0.002							,    _snake_case = 80.0							,    _snake_case = 0.5							,    _snake_case = 1.0							,    _snake_case = 7.0							,    _snake_case = True							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      sigma_max

  lowerCAmelCase  							=      np.linspace(0							,    1							,    _snake_case			)
  lowerCAmelCase  							=      self._convert_to_karras(_snake_case			)
  lowerCAmelCase  							=      self.sigma_to_t(_snake_case			)

  # setable values
  lowerCAmelCase  							=      None
  lowerCAmelCase  							=      torch.from_numpy(_snake_case			)
  lowerCAmelCase  							=      torch.from_numpy(_snake_case			)
  lowerCAmelCase  							=      False
  lowerCAmelCase  							=      False



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







  """simple docstring"""






  if schedule_timesteps is None:
   lowerCAmelCase  							=      self.timesteps

  lowerCAmelCase  							=      (schedule_timesteps == timestep).nonzero()
  return indices.item()



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  if isinstance(_snake_case							,    torch.Tensor			):
   lowerCAmelCase  							=      timestep.to(self.timesteps.device			)
  lowerCAmelCase  							=      self.index_for_timestep(_snake_case			)
  lowerCAmelCase  							=      self.sigmas[step_idx]

  lowerCAmelCase  							=      sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

  lowerCAmelCase  							=      True
  return sample



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  if not isinstance(_snake_case							,    np.ndarray			):
   lowerCAmelCase  							=      np.array(_snake_case							,    dtype=np.floataa			)

  lowerCAmelCase  							=      10_00 * 0.25 * np.log(sigmas + 1E-44			)

  return timesteps



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







  """simple docstring"""






  if num_inference_steps is None and timesteps is None:
   raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'			)

  if num_inference_steps is not None and timesteps is not None:
   raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'			)

  # Follow DDPMScheduler custom timesteps logic
  if timesteps is not None:
   for i in range(1							,    len(_snake_case			)			):
    if timesteps[i] >= timesteps[i - 1]:
     raise ValueError('`timesteps` must be in descending order.'			)

   if timesteps[0] >= self.config.num_train_timesteps:
    raise ValueError(
        F'`timesteps` must start before `self.config.train_timesteps`:'
        F' {self.config.num_train_timesteps}.'			)

   lowerCAmelCase  							=      np.array(_snake_case							,    dtype=np.intaa			)
   lowerCAmelCase  							=      True
  else:
   if num_inference_steps > self.config.num_train_timesteps:
    raise ValueError(
        F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
        F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
        F' maximal {self.config.num_train_timesteps} timesteps.'			)

   lowerCAmelCase  							=      num_inference_steps

   lowerCAmelCase  							=      self.config.num_train_timesteps // self.num_inference_steps
   lowerCAmelCase  							=      (np.arange(0							,    _snake_case			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
   lowerCAmelCase  							=      False

  # Map timesteps to Karras sigmas directly for multistep sampling
  # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
  lowerCAmelCase  							=      self.config.num_train_timesteps
  lowerCAmelCase  							=      timesteps[::-1].copy()
  lowerCAmelCase  							=      ramp / (num_train_timesteps - 1)
  lowerCAmelCase  							=      self._convert_to_karras(_snake_case			)
  lowerCAmelCase  							=      self.sigma_to_t(_snake_case			)

  lowerCAmelCase  							=      np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
  lowerCAmelCase  							=      torch.from_numpy(_snake_case			).to(device=_snake_case			)

  if str(_snake_case			).startswith('mps'			):
   # mps does not support float64
   lowerCAmelCase  							=      torch.from_numpy(_snake_case			).to(_snake_case							,    dtype=torch.floataa			)
  else:
   lowerCAmelCase  							=      torch.from_numpy(_snake_case			).to(device=_snake_case			)



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.config.sigma_min
  lowerCAmelCase  							=      self.config.sigma_max

  lowerCAmelCase  							=      self.config.rho
  lowerCAmelCase  							=      sigma_min ** (1 / rho)
  lowerCAmelCase  							=      sigma_max ** (1 / rho)
  lowerCAmelCase  							=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  return sigmas



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.config.sigma_data

  lowerCAmelCase  							=      sigma_data**2 / (sigma**2 + sigma_data**2)
  lowerCAmelCase  							=      sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  return c_skip, c_out



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.config.sigma_min
  lowerCAmelCase  							=      self.config.sigma_data

  lowerCAmelCase  							=      sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
  lowerCAmelCase  							=      (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  return c_skip, c_out



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







  """simple docstring"""






  if (
      isinstance(_snake_case							,    _snake_case			)
      or isinstance(_snake_case							,    torch.IntTensor			)
      or isinstance(_snake_case							,    torch.LongTensor			)
  ):
   raise ValueError(
       (
           'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
           F' `{self.__class__}.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(_snake_case							,    torch.Tensor			):
   lowerCAmelCase  							=      timestep.to(self.timesteps.device			)

  lowerCAmelCase  							=      self.config.sigma_min
  lowerCAmelCase  							=      self.config.sigma_max

  lowerCAmelCase  							=      self.index_for_timestep(_snake_case			)

  # sigma_next corresponds to next_t in original implementation
  lowerCAmelCase  							=      self.sigmas[step_index]
  if step_index + 1 < self.config.num_train_timesteps:
   lowerCAmelCase  							=      self.sigmas[step_index + 1]
  else:
   # Set sigma_next to sigma_min
   lowerCAmelCase  							=      self.sigmas[-1]

  # Get scalings for boundary conditions
  lowerCAmelCase							,lowerCAmelCase  							=      self.get_scalings_for_boundary_condition(_snake_case			)

  # 1. Denoise model output using boundary conditions
  lowerCAmelCase  							=      c_out * model_output + c_skip * sample
  if self.config.clip_denoised:
   lowerCAmelCase  							=      denoised.clamp(-1							,    1			)

  # 2. Sample z ~ N(0, s_noise^2 * I)
  # Noise is not used for onestep sampling.
  if len(self.timesteps			) > 1:
   lowerCAmelCase  							=      randn_tensor(
       model_output.shape							,    dtype=model_output.dtype							,    device=model_output.device							,    generator=_snake_case			)
  else:
   lowerCAmelCase  							=      torch.zeros_like(_snake_case			)
  lowerCAmelCase  							=      noise * self.config.s_noise

  lowerCAmelCase  							=      sigma_next.clamp(min=_snake_case							,    max=_snake_case			)

  # 3. Return noisy sample
  # tau = sigma_hat, eps = sigma_min
  lowerCAmelCase  							=      denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

  if not return_dict:
   return (prev_sample,)

  return CMStochasticIterativeSchedulerOutput(prev_sample=_snake_case			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      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
   lowerCAmelCase  							=      self.timesteps.to(original_samples.device							,    dtype=torch.floataa			)
   lowerCAmelCase  							=      timesteps.to(original_samples.device							,    dtype=torch.floataa			)
  else:
   lowerCAmelCase  							=      self.timesteps.to(original_samples.device			)
   lowerCAmelCase  							=      timesteps.to(original_samples.device			)

  lowerCAmelCase  							=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

  lowerCAmelCase  							=      original_samples + noise * sigma
  return noisy_samples



 def __len__(			self			):







  """simple docstring"""






  return self.config.num_train_timesteps












"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.mpnet.modeling_tf_mpnet import (
           TFMPNetForMaskedLM,
           TFMPNetForMultipleChoice,
           TFMPNetForQuestionAnswering,
           TFMPNetForSequenceClassification,
           TFMPNetForTokenClassification,
           TFMPNetModel,
       )







class 	a				:



 def __init__(			self							,    _snake_case							,    _snake_case=13							,    _snake_case=7							,    _snake_case=True							,    _snake_case=True							,    _snake_case=False							,    _snake_case=True							,    _snake_case=99							,    _snake_case=64							,    _snake_case=2							,    _snake_case=4							,    _snake_case=64							,    _snake_case="gelu"							,    _snake_case=0.1							,    _snake_case=0.1							,    _snake_case=5_12							,    _snake_case=16							,    _snake_case=2							,    _snake_case=0.02							,    _snake_case=3							,    _snake_case=4							,    _snake_case=None							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      parent
  lowerCAmelCase  							=      batch_size
  lowerCAmelCase  							=      seq_length
  lowerCAmelCase  							=      is_training
  lowerCAmelCase  							=      use_input_mask
  lowerCAmelCase  							=      use_token_type_ids
  lowerCAmelCase  							=      use_labels
  lowerCAmelCase  							=      vocab_size
  lowerCAmelCase  							=      hidden_size
  lowerCAmelCase  							=      num_hidden_layers
  lowerCAmelCase  							=      num_attention_heads
  lowerCAmelCase  							=      intermediate_size
  lowerCAmelCase  							=      hidden_act
  lowerCAmelCase  							=      hidden_dropout_prob
  lowerCAmelCase  							=      attention_probs_dropout_prob
  lowerCAmelCase  							=      max_position_embeddings
  lowerCAmelCase  							=      type_vocab_size
  lowerCAmelCase  							=      type_sequence_label_size
  lowerCAmelCase  							=      initializer_range
  lowerCAmelCase  							=      num_labels
  lowerCAmelCase  							=      num_choices
  lowerCAmelCase  							=      scope



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size			)

  lowerCAmelCase  							=      None
  if self.use_input_mask:
   lowerCAmelCase  							=      random_attention_mask([self.batch_size, self.seq_length]			)

  lowerCAmelCase  							=      None
  lowerCAmelCase  							=      None
  lowerCAmelCase  							=      None
  if self.use_labels:
   lowerCAmelCase  							=      ids_tensor([self.batch_size]							,    self.type_sequence_label_size			)
   lowerCAmelCase  							=      ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels			)
   lowerCAmelCase  							=      ids_tensor([self.batch_size]							,    self.num_choices			)

  lowerCAmelCase  							=      MPNetConfig(
      vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    initializer_range=self.initializer_range							,    )
  return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetModel(config=_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  lowerCAmelCase  							=      [input_ids, input_mask]
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetForMaskedLM(config=_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetForQuestionAnswering(config=_snake_case			)
  lowerCAmelCase  							=      {
      'input_ids': input_ids,
      'attention_mask': input_mask,
  }
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.start_logits.shape							,    (self.batch_size, self.seq_length)			)
  self.parent.assertEqual(result.end_logits.shape							,    (self.batch_size, self.seq_length)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_labels
  lowerCAmelCase  							=      TFMPNetForSequenceClassification(_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_choices
  lowerCAmelCase  							=      TFMPNetForMultipleChoice(_snake_case			)
  lowerCAmelCase  							=      tf.tile(tf.expand_dims(_snake_case							,    1			)							,    (1, self.num_choices, 1)			)
  lowerCAmelCase  							=      tf.tile(tf.expand_dims(_snake_case							,    1			)							,    (1, self.num_choices, 1)			)
  lowerCAmelCase  							=      {
      'input_ids': multiple_choice_inputs_ids,
      'attention_mask': multiple_choice_input_mask,
  }
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_choices)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_labels
  lowerCAmelCase  							=      TFMPNetForTokenClassification(_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.prepare_config_and_inputs()
  ((lowerCAmelCase)							,(lowerCAmelCase)							,(lowerCAmelCase)							,(lowerCAmelCase)							,(lowerCAmelCase)							,(lowerCAmelCase))  							=      config_and_inputs
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  return config, inputs_dict







@require_tf
class 	a				(					a__					,      a__					,      unittest.TestCase   ):
 snake_case__    	=    (
     (
         TFMPNetForMaskedLM,
         TFMPNetForMultipleChoice,
         TFMPNetForQuestionAnswering,
         TFMPNetForSequenceClassification,
         TFMPNetForTokenClassification,
         TFMPNetModel,
     )
     if is_tf_available()
     else ()
 )
 snake_case__    	=    (
     {
         '''feature-extraction''': TFMPNetModel,
         '''fill-mask''': TFMPNetForMaskedLM,
         '''question-answering''': TFMPNetForQuestionAnswering,
         '''text-classification''': TFMPNetForSequenceClassification,
         '''token-classification''': TFMPNetForTokenClassification,
         '''zero-shot''': TFMPNetForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 snake_case__    	=    False
 snake_case__    	=    False



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetModelTester(self			)
  lowerCAmelCase  							=      ConfigTester(self							,    config_class=_snake_case							,    hidden_size=37			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_model(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_masked_lm(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_question_answering(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_sequence_classification(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_multiple_choice(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_token_classification(*_snake_case			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for model_name in ["microsoft/mpnet-base"]:
   lowerCAmelCase  							=      TFMPNetModel.from_pretrained(_snake_case			)
   self.assertIsNotNone(_snake_case			)







@require_tf
class 	a				(					unittest.TestCase   ):



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetModel.from_pretrained('microsoft/mpnet-base'			)
  lowerCAmelCase  							=      tf.constant([[0, 1, 2, 3, 4, 5]]			)
  lowerCAmelCase  							=      model(_snake_case			)[0]

  lowerCAmelCase  							=      [1, 6, 7_68]
  self.assertEqual(output.shape							,    _snake_case			)

  lowerCAmelCase  							=      tf.constant(
      [
          [
              [-0.1_067_172, 0.08_216_473, 0.0_024_543],
              [-0.03_465_879, 0.8_354_118, -0.03_252_288],
              [-0.06_569_476, -0.12_424_111, -0.0_494_436],
          ]
      ]			)
  tf.debugging.assert_near(output[:, :3, :3]							,    _snake_case							,    atol=1E-4			)





