



'''simple docstring'''


from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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 import (
       TFConvBertForMaskedLM,
       TFConvBertForMultipleChoice,
       TFConvBertForQuestionAnswering,
       TFConvBertForSequenceClassification,
       TFConvBertForTokenClassification,
       TFConvBertModel,
   )

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]=1_3  ,						UpperCamelCase__      :					Optional[Any]=7  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					Any=True  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					Optional[Any]=True  ,						UpperCamelCase__      :					Dict=9_9  ,						UpperCamelCase__      :					Dict=3_2  ,						UpperCamelCase__      :					Tuple=2  ,						UpperCamelCase__      :					Any=4  ,						UpperCamelCase__      :					Optional[Any]=3_7  ,						UpperCamelCase__      :					List[str]="gelu"  ,						UpperCamelCase__      :					Any=0.1  ,						UpperCamelCase__      :					Tuple=0.1  ,						UpperCamelCase__      :					str=5_1_2  ,						UpperCamelCase__      :					Optional[int]=1_6  ,						UpperCamelCase__      :					Optional[int]=2  ,						UpperCamelCase__      :					Tuple=0.0_2  ,						UpperCamelCase__      :					Any=3  ,						UpperCamelCase__      :					Any=4  ,						UpperCamelCase__      :					List[Any]=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  1_3
       UpperCamelCase					=  7
       UpperCamelCase					=  True
       UpperCamelCase					=  True
       UpperCamelCase					=  True
       UpperCamelCase					=  True
       UpperCamelCase					=  9_9
       UpperCamelCase					=  3_8_4
       UpperCamelCase					=  2
       UpperCamelCase					=  4
       UpperCamelCase					=  3_7
       UpperCamelCase					=  'gelu'
       UpperCamelCase					=  0.1
       UpperCamelCase					=  0.1
       UpperCamelCase					=  5_1_2
       UpperCamelCase					=  1_6
       UpperCamelCase					=  2
       UpperCamelCase					=  0.0_2
       UpperCamelCase					=  3
       UpperCamelCase					=  4
       UpperCamelCase					=  1_2_8
       UpperCamelCase					=  2
       UpperCamelCase					=  9
       UpperCamelCase					=  1
       UpperCamelCase					=  None






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







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

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

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

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

       UpperCamelCase					=  ConvBertConfig(
           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  ,						initializer_range=self.initializer_range  ,						return_dict=UpperCamelCase__  ,						)

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  TFConvBertModel(config=UpperCamelCase__ )
       UpperCamelCase					=  {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

       UpperCamelCase					=  [input_ids, input_mask]
       UpperCamelCase					=  model(UpperCamelCase__ )

       UpperCamelCase					=  model(UpperCamelCase__ )

       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  TFConvBertForMaskedLM(config=UpperCamelCase__ )
       UpperCamelCase					=  {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size) )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  TFConvBertForSequenceClassification(config=UpperCamelCase__ )
       UpperCamelCase					=  {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.num_choices
       UpperCamelCase					=  TFConvBertForMultipleChoice(config=UpperCamelCase__ )
       UpperCamelCase					=  tf.tile(tf.expand_dims(UpperCamelCase__  ,						1 )  ,						(1, self.num_choices, 1) )
       UpperCamelCase					=  tf.tile(tf.expand_dims(UpperCamelCase__  ,						1 )  ,						(1, self.num_choices, 1) )
       UpperCamelCase					=  tf.tile(tf.expand_dims(UpperCamelCase__  ,						1 )  ,						(1, self.num_choices, 1) )
       UpperCamelCase					=  {
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  TFConvBertForTokenClassification(config=UpperCamelCase__ )
       UpperCamelCase					=  {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels) )






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  TFConvBertForQuestionAnswering(config=UpperCamelCase__ )
       UpperCamelCase					=  {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       UpperCamelCase					=  model(UpperCamelCase__ )
       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  A       (  self      :					Dict ):


       """simple docstring"""







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

@require_tf
class  SCREAMING_SNAKE_CASE (  _a   ,						_a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            TFConvBertModel,
            TFConvBertForMaskedLM,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  (
        {
            """feature-extraction""": TFConvBertModel,
            """fill-mask""": TFConvBertForMaskedLM,
            """question-answering""": TFConvBertForQuestionAnswering,
            """text-classification""": TFConvBertForSequenceClassification,
            """token-classification""": TFConvBertForTokenClassification,
            """zero-shot""": TFConvBertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  TFConvBertModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					str ):


       """simple docstring"""







       self.config_tester.run_common_tests()






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase					=  True
       UpperCamelCase					=  True

       if hasattr(UpperCamelCase__  ,						'use_cache' ):
          UpperCamelCase					=  True

       UpperCamelCase					=  getattr(self.model_tester  ,						'encoder_seq_length'  ,						self.model_tester.seq_length )
       UpperCamelCase					=  getattr(self.model_tester  ,						'key_length'  ,						UpperCamelCase__ )

       for model_class in self.all_model_classes:
          UpperCamelCase					=  self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          UpperCamelCase					=  len(model(UpperCamelCase__ ) )

          with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(UpperCamelCase__  ,						saved_model=UpperCamelCase__ )
             UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'saved_model'  ,						'1' )
             UpperCamelCase					=  tf.keras.models.load_model(UpperCamelCase__ )
             UpperCamelCase					=  model(UpperCamelCase__ )

             if self.is_encoder_decoder:
                UpperCamelCase					=  outputs['encoder_hidden_states']
                UpperCamelCase					=  outputs['encoder_attentions']
             else:
                UpperCamelCase					=  outputs['hidden_states']
                UpperCamelCase					=  outputs['attentions']

             self.assertEqual(len(UpperCamelCase__ )  ,						UpperCamelCase__ )

             UpperCamelCase					=  getattr(
                 self.model_tester  ,						'expected_num_hidden_layers'  ,						self.model_tester.num_hidden_layers + 1 )

             self.assertEqual(len(UpperCamelCase__ )  ,						UpperCamelCase__ )
             self.assertListEqual(
                 list(output_hidden_states[0].shape[-2:] )  ,						[self.model_tester.seq_length, self.model_tester.hidden_size]  ,						)

             self.assertEqual(len(UpperCamelCase__ )  ,						self.model_tester.num_hidden_layers )
             self.assertListEqual(
                 list(output_attentions[0].shape[-3:] )  ,						[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]  ,						)






    @slow
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
       self.assertIsNotNone(UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase					=  True
       UpperCamelCase					=  getattr(self.model_tester  ,						'decoder_seq_length'  ,						self.model_tester.seq_length )
       UpperCamelCase					=  getattr(self.model_tester  ,						'encoder_seq_length'  ,						self.model_tester.seq_length )
       UpperCamelCase					=  getattr(self.model_tester  ,						'key_length'  ,						UpperCamelCase__ )
       UpperCamelCase					=  getattr(self.model_tester  ,						'key_length'  ,						UpperCamelCase__ )

       def check_decoder_attentions_output(UpperCamelCase__      :					List[Any] ):
          UpperCamelCase					=  len(UpperCamelCase__ )
          self.assertEqual(out_len % 2  ,						0 )
          UpperCamelCase					=  outputs.decoder_attentions
          self.assertEqual(len(UpperCamelCase__ )  ,						self.model_tester.num_hidden_layers )
          self.assertListEqual(
              list(decoder_attentions[0].shape[-3:] )  ,						[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]  ,						)

       def check_encoder_attentions_output(UpperCamelCase__      :					Dict ):
          UpperCamelCase					=  [
              t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
          ]
          self.assertEqual(len(UpperCamelCase__ )  ,						self.model_tester.num_hidden_layers )
          self.assertListEqual(
              list(attentions[0].shape[-3:] )  ,						[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]  ,						)

       for model_class in self.all_model_classes:
          UpperCamelCase					=  True
          UpperCamelCase					=  False
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          UpperCamelCase					=  model(self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )
          UpperCamelCase					=  len(UpperCamelCase__ )
          self.assertEqual(config.output_hidden_states  ,						UpperCamelCase__ )
          check_encoder_attentions_output(UpperCamelCase__ )

          if self.is_encoder_decoder:
             UpperCamelCase					=  model_class(UpperCamelCase__ )
             UpperCamelCase					=  model(self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )
             self.assertEqual(config.output_hidden_states  ,						UpperCamelCase__ )
             check_decoder_attentions_output(UpperCamelCase__ )

          # Check that output attentions can also be changed via the config
          del inputs_dict["output_attentions"]
          UpperCamelCase					=  True
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          UpperCamelCase					=  model(self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )
          self.assertEqual(config.output_hidden_states  ,						UpperCamelCase__ )
          check_encoder_attentions_output(UpperCamelCase__ )

          # Check attention is always last and order is fine
          UpperCamelCase					=  True
          UpperCamelCase					=  True
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          UpperCamelCase					=  model(self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )

          self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)  ,						len(UpperCamelCase__ ) )
          self.assertEqual(model.config.output_hidden_states  ,						UpperCamelCase__ )
          check_encoder_attentions_output(UpperCamelCase__ )

@require_tf
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @slow
    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
       UpperCamelCase					=  tf.constant([[0, 1, 2, 3, 4, 5]] )
       UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  [1, 6, 7_6_8]
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  tf.constant(
           [
               [
                   [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
                   [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
                   [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
               ]
           ] )
       tf.debugging.assert_near(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 )






'''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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None







def 				__lowerCamelCase				(   A__    ,						A__=0.999    ,						A__="cosine"    ,						)       ->	Tuple:






   """simple docstring"""
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   UpperCamelCase					=  []
   for i in range(A__    ):
      UpperCamelCase					=  i / num_diffusion_timesteps
      UpperCamelCase					=  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(A__    ) / alpha_bar_fn(A__    )    ,						A__    )    )
   return torch.tensor(A__    ,						dtype=torch.floataa    )

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    @register_to_config
    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					int = 1_0_0_0  ,						UpperCamelCase__      :					str = "fixed_small_log"  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[float] = 1.0  ,						UpperCamelCase__      :					str = "epsilon"  ,						UpperCamelCase__      :					str = "squaredcos_cap_v2"  ,						):


       """simple docstring"""







       if beta_schedule != "squaredcos_cap_v2":
          raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )

       UpperCamelCase					=  betas_for_alpha_bar(UpperCamelCase__ )

       UpperCamelCase					=  1.0 - self.betas
       UpperCamelCase					=  torch.cumprod(self.alphas  ,						dim=0 )
       UpperCamelCase					=  torch.tensor(1.0 )

       # standard deviation of the initial noise distribution
       UpperCamelCase					=  1.0

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  torch.from_numpy(np.arange(0  ,						UpperCamelCase__ )[::-1].copy() )

       UpperCamelCase					=  variance_type






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       UpperCamelCase					=  (np.arange(0  ,						UpperCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Tuple=None ):


       """simple docstring"""







       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
       else:
          UpperCamelCase					=  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
       UpperCamelCase					=  beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
          UpperCamelCase					=  self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
          UpperCamelCase					=  torch.log(torch.clamp(UpperCamelCase__  ,						min=1E-2_0 ) )
          UpperCamelCase					=  torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
          # NOTE difference with DDPM scheduler
          UpperCamelCase					=  variance.log()
          UpperCamelCase					=  beta.log()

          UpperCamelCase					=  (predicted_variance + 1) / 2
          UpperCamelCase					=  frac * max_log + (1 - frac) * min_log

       return variance






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
          UpperCamelCase				,      UpperCamelCase					=  torch.split(UpperCamelCase__  ,						sample.shape[1]  ,						dim=1 )
       else:
          UpperCamelCase					=  None

       # 1. compute alphas, betas
       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
          UpperCamelCase					=  self.alphas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev
          UpperCamelCase					=  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":
          UpperCamelCase					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
          UpperCamelCase					=  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:
          UpperCamelCase					=  torch.clamp(
              UpperCamelCase__  ,						-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
       UpperCamelCase					=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       UpperCamelCase					=  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
       UpperCamelCase					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       UpperCamelCase					=  0
       if t > 0:
          UpperCamelCase					=  randn_tensor(
              model_output.shape  ,						dtype=model_output.dtype  ,						generator=UpperCamelCase__  ,						device=model_output.device )

          UpperCamelCase					=  self._get_variance(
              UpperCamelCase__  ,						predicted_variance=UpperCamelCase__  ,						prev_timestep=UpperCamelCase__  ,						)

          if self.variance_type == "fixed_small_log":
             UpperCamelCase					=  variance
          elif self.variance_type == "learned_range":
             UpperCamelCase					=  (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.' )

          UpperCamelCase					=  variance * variance_noise

       UpperCamelCase					=  pred_prev_sample + variance

       if not return_dict:
          return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.IntTensor  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.alphas_cumprod.to(device=original_samples.device  ,						dtype=original_samples.dtype )
       UpperCamelCase					=  timesteps.to(original_samples.device )

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

       UpperCamelCase					=  (1 - alphas_cumprod[timesteps]) ** 0.5
       UpperCamelCase					=  sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
          UpperCamelCase					=  sqrt_one_minus_alpha_prod.unsqueeze(-1 )

       UpperCamelCase					=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples

