
'''simple docstring'''






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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		None







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    List[Any]=0.9_9_9				,	_lowerCAmelCase   :    Any="cosine"				,	)     ->   Optional[Any]:



      """simple docstring"""




      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_lowerCAmelCase   :    List[Any]							):
                  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[Any]							):
                  return math.exp(t * -12.0							)

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

      _lowerCamelCase				:   Tuple								=   []
      for i in range(_lowerCAmelCase							):
            _lowerCamelCase				:   int								=   i / num_diffusion_timesteps
            _lowerCamelCase				:   str								=   (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__						(       A						,	A ):



    @register_to_config
    def __init__(	self      :       Any,__A      :       int = 1_0_0_0,__A      :       str = "fixed_small_log",__A      :       bool = True,__A      :       Optional[float] = 1.0,__A      :       str = "epsilon",__A      :       str = "squaredcos_cap_v2",):
          if beta_schedule != "squaredcos_cap_v2":
                raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"     )

          _lowerCamelCase				:   Union[str, Any]								=   betas_for_alpha_bar(__A     )

          _lowerCamelCase				:   str								=   1.0 - self.betas
          _lowerCamelCase				:   Any								=   torch.cumprod(self.alphas,dim=0     )
          _lowerCamelCase				:   str								=   torch.tensor(1.0     )

          # standard deviation of the initial noise distribution
          _lowerCamelCase				:   Optional[Any]								=   1.0

          # setable values
          _lowerCamelCase				:   Optional[Any]								=   None
          _lowerCamelCase				:   str								=   torch.from_numpy(np.arange(0,__A     )[::-1].copy()     )

          _lowerCamelCase				:   Optional[int]								=   variance_type



    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       torch.FloatTensor,__A      :       Optional[int] = None     ):
          return sample



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       int,__A      :       Union[str, torch.device] = None     ):
          _lowerCamelCase				:   Dict								=   num_inference_steps
          _lowerCamelCase				:   List[str]								=   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
          _lowerCamelCase				:   Union[str, Any]								=   (np.arange(0,__A     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
          _lowerCamelCase				:   List[str]								=   torch.from_numpy(__A     ).to(__A     )



    def   lowerCamelCase_  (	self      :       str,__A      :       Optional[Any],__A      :       str=None,__A      :       str=None,__A      :       Union[str, Any]=None     ):
          if prev_timestep is None:
                _lowerCamelCase				:   Optional[int]								=   t - 1

          _lowerCamelCase				:   str								=   self.alphas_cumprod[t]
          _lowerCamelCase				:   Optional[Any]								=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          _lowerCamelCase				:   int								=   1 - alpha_prod_t
          _lowerCamelCase				:   Union[str, Any]								=   1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
                _lowerCamelCase				:   Any								=   self.betas[t]
          else:
                _lowerCamelCase				:   Optional[Any]								=   1 - alpha_prod_t / 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
          _lowerCamelCase				:   str								=   beta_prod_t_prev / beta_prod_t * beta

          if variance_type is None:
                _lowerCamelCase				:   Tuple								=   self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small_log":
                _lowerCamelCase				:   Tuple								=   torch.log(torch.clamp(__A,min=1e-20     )     )
                _lowerCamelCase				:   Optional[Any]								=   torch.exp(0.5 * variance     )
          elif variance_type == "learned_range":
                # NOTE difference with DDPM scheduler
                _lowerCamelCase				:   Optional[int]								=   variance.log()
                _lowerCamelCase				:   List[Any]								=   beta.log()

                _lowerCamelCase				:   Optional[Any]								=   (predicted_variance + 1) / 2
                _lowerCamelCase				:   List[Any]								=   frac * max_log + (1 - frac) * min_log

          return variance



    def   lowerCamelCase_  (	self      :       int,__A      :       torch.FloatTensor,__A      :       int,__A      :       torch.FloatTensor,__A      :       Optional[int] = None,__A      :       Optional[Any]=None,__A      :       bool = True,):
          _lowerCamelCase				:   str								=   timestep

          if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                _lowerCamelCase				:   Optional[Any]								=   torch.split(__A,sample.shape[1],dim=1     )
          else:
                _lowerCamelCase				:   int								=   None

          # 1. compute alphas, betas
          if prev_timestep is None:
                _lowerCamelCase				:   Optional[Any]								=   t - 1

          _lowerCamelCase				:   str								=   self.alphas_cumprod[t]
          _lowerCamelCase				:   Tuple								=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          _lowerCamelCase				:   Any								=   1 - alpha_prod_t
          _lowerCamelCase				:   Optional[int]								=   1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
                _lowerCamelCase				:   Optional[Any]								=   self.betas[t]
                _lowerCamelCase				:   str								=   self.alphas[t]
          else:
                _lowerCamelCase				:   List[Any]								=   1 - alpha_prod_t / alpha_prod_t_prev
                _lowerCamelCase				:   Dict								=   1 - beta

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          if self.config.prediction_type == "epsilon":
                _lowerCamelCase				:   List[Any]								=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
                _lowerCamelCase				:   str								=   model_output
          else:
                raise ValueError(
                    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                    " for the UnCLIPScheduler."     )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
                _lowerCamelCase				:   int								=   torch.clamp(
                    __A,-self.config.clip_sample_range,self.config.clip_sample_range     )

          # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          _lowerCamelCase				:   Tuple								=   (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
          _lowerCamelCase				:   Optional[Any]								=   alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

          # 5. Compute predicted previous sample µ_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          _lowerCamelCase				:   Union[str, Any]								=   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          _lowerCamelCase				:   List[Any]								=   0
          if t > 0:
                _lowerCamelCase				:   Tuple								=   randn_tensor(
                    model_output.shape,dtype=model_output.dtype,generator=__A,device=model_output.device     )

                _lowerCamelCase				:   Optional[Any]								=   self._get_variance(
                    __A,predicted_variance=__A,prev_timestep=__A,)

                if self.variance_type == "fixed_small_log":
                      _lowerCamelCase				:   Union[str, Any]								=   variance
                elif self.variance_type == "learned_range":
                      _lowerCamelCase				:   Any								=   (0.5 * variance).exp()
                else:
                      raise ValueError(
                          f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                          " for the UnCLIPScheduler."     )

                _lowerCamelCase				:   Tuple								=   variance * variance_noise

          _lowerCamelCase				:   List[Any]								=   pred_prev_sample + variance

          if not return_dict:
                return (pred_prev_sample,)

          return UnCLIPSchedulerOutput(prev_sample=__A,pred_original_sample=__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       torch.FloatTensor,__A      :       torch.FloatTensor,__A      :       torch.IntTensor,):
          # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
          _lowerCamelCase				:   Union[str, Any]								=   self.alphas_cumprod.to(device=original_samples.device,dtype=original_samples.dtype     )
          _lowerCamelCase				:   str								=   timesteps.to(original_samples.device     )

          _lowerCamelCase				:   str								=   alphas_cumprod[timesteps] ** 0.5
          _lowerCamelCase				:   Dict								=   sqrt_alpha_prod.flatten()
          while len(sqrt_alpha_prod.shape     ) < len(original_samples.shape     ):
                _lowerCamelCase				:   str								=   sqrt_alpha_prod.unsqueeze(-1     )

          _lowerCamelCase				:   List[Any]								=   (1 - alphas_cumprod[timesteps]) ** 0.5
          _lowerCamelCase				:   Optional[int]								=   sqrt_one_minus_alpha_prod.flatten()
          while len(sqrt_one_minus_alpha_prod.shape     ) < len(original_samples.shape     ):
                _lowerCamelCase				:   Optional[Any]								=   sqrt_one_minus_alpha_prod.unsqueeze(-1     )

          _lowerCamelCase				:   Dict								=   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
          return noisy_samples

'''simple docstring'''






import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Union[str, Any],__A      :       Optional[int]=1_3,__A      :       Optional[Any]=7,__A      :       int=True,__A      :       Any=True,__A      :       Dict=True,__A      :       List[Any]=True,__A      :       Optional[Any]=9_9,__A      :       List[str]=3_2,__A      :       int=5,__A      :       str=4,__A      :       int=3_7,__A      :       Optional[Any]="gelu",__A      :       Union[str, Any]=0.1,__A      :       str=0.1,__A      :       Any=5_1_2,__A      :       Any=1_6,__A      :       Optional[int]=2,__A      :       int=0.02,__A      :       Optional[int]=4,):
          _lowerCamelCase				:   Union[str, Any]								=   parent
          _lowerCamelCase				:   Tuple								=   batch_size
          _lowerCamelCase				:   List[str]								=   seq_length
          _lowerCamelCase				:   Optional[int]								=   is_training
          _lowerCamelCase				:   str								=   use_attention_mask
          _lowerCamelCase				:   str								=   use_token_type_ids
          _lowerCamelCase				:   List[str]								=   use_labels
          _lowerCamelCase				:   Tuple								=   vocab_size
          _lowerCamelCase				:   Dict								=   hidden_size
          _lowerCamelCase				:   str								=   num_hidden_layers
          _lowerCamelCase				:   List[str]								=   num_attention_heads
          _lowerCamelCase				:   Optional[Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[int]								=   hidden_act
          _lowerCamelCase				:   Any								=   hidden_dropout_prob
          _lowerCamelCase				:   List[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   Tuple								=   max_position_embeddings
          _lowerCamelCase				:   str								=   type_vocab_size
          _lowerCamelCase				:   str								=   type_sequence_label_size
          _lowerCamelCase				:   int								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[Any]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Union[str, Any]								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Tuple								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   Any								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   int								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   int								=   AlbertConfig(
              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,type_vocab_size=self.type_vocab_size,is_decoder=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   str								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   config_and_inputs
          _lowerCamelCase				:   Any								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   FlaxAlbertModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   str								=   model_class_name.from_pretrained("albert-base-v2"     )
                _lowerCamelCase				:   Any								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )


@require_flax
class  UpperCAmelCase__						(       unittest.TestCase ):



    @slow
    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Optional[int]								=   FlaxAlbertModel.from_pretrained("albert-base-v2"     )
          _lowerCamelCase				:   int								=   np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
          _lowerCamelCase				:   Union[str, Any]								=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
          _lowerCamelCase				:   Optional[Any]								=   model(__A,attention_mask=__A     )[0]
          _lowerCamelCase				:   Dict								=   (1, 1_1, 7_6_8)
          self.assertEqual(output.shape,__A     )
          _lowerCamelCase				:   Tuple								=   np.array(
              [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]     )

          self.assertTrue(jnp.allclose(output[:, 1:4, 1:4],__A,atol=1e-4     )     )