import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        YosoForMaskedLM,
        YosoForMultipleChoice,
        YosoForQuestionAnswering,
        YosoForSequenceClassification,
        YosoForTokenClassification,
        YosoModel,
    )
    from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class 						__snake_case						:





      '''simple docstring'''






      def __init__(       self  :					List[Any]     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Tuple=13     ,	_UpperCamelCase  :					Union[str, Any]=7     ,	_UpperCamelCase  :					Union[str, Any]=True     ,	_UpperCamelCase  :					Dict=True     ,	_UpperCamelCase  :					List[Any]=True     ,	_UpperCamelCase  :					Optional[Any]=True     ,	_UpperCamelCase  :					Optional[int]=99     ,	_UpperCamelCase  :					List[Any]=32     ,	_UpperCamelCase  :					Dict=5     ,	_UpperCamelCase  :					Dict=4     ,	_UpperCamelCase  :					Optional[int]=37     ,	_UpperCamelCase  :					List[Any]="gelu"     ,	_UpperCamelCase  :					int=0.1     ,	_UpperCamelCase  :					str=0.1     ,	_UpperCamelCase  :					int=512     ,	_UpperCamelCase  :					Optional[Any]=16     ,	_UpperCamelCase  :					Tuple=2     ,	_UpperCamelCase  :					Any=0.0_2     ,	_UpperCamelCase  :					Any=3     ,	_UpperCamelCase  :					Union[str, Any]=4     ,	_UpperCamelCase  :					List[Any]=None     ,	)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    parent
          _lowerCamelCase  :		List[Any]			    =    batch_size
          _lowerCamelCase  :		int			    =    seq_length
          _lowerCamelCase  :		List[Any]			    =    is_training
          _lowerCamelCase  :		List[Any]			    =    use_input_mask
          _lowerCamelCase  :		Tuple			    =    use_token_type_ids
          _lowerCamelCase  :		int			    =    use_labels
          _lowerCamelCase  :		int			    =    vocab_size
          _lowerCamelCase  :		List[str]			    =    hidden_size
          _lowerCamelCase  :		List[Any]			    =    num_hidden_layers
          _lowerCamelCase  :		str			    =    num_attention_heads
          _lowerCamelCase  :		List[str]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Optional[int]			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[int]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Tuple			    =    max_position_embeddings
          _lowerCamelCase  :		List[str]			    =    type_vocab_size
          _lowerCamelCase  :		Dict			    =    type_sequence_label_size
          _lowerCamelCase  :		str			    =    initializer_range
          _lowerCamelCase  :		Tuple			    =    num_labels
          _lowerCamelCase  :		Union[str, Any]			    =    num_choices
          _lowerCamelCase  :		str			    =    scope



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->str:


          """simple docstring"""


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

          _lowerCamelCase  :		Tuple			    =    None
          if self.use_input_mask:
              _lowerCamelCase  :		Dict			    =    random_attention_mask([self.batch_size, self.seq_length])

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

          _lowerCamelCase  :		List[str]			    =    None
          _lowerCamelCase  :		Optional[int]			    =    None
          _lowerCamelCase  :		Dict			    =    None
          if self.use_labels:
              _lowerCamelCase  :		List[str]			    =    ids_tensor([self.batch_size]     ,	self.type_sequence_label_size)
              _lowerCamelCase  :		List[str]			    =    ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels)
              _lowerCamelCase  :		Any			    =    ids_tensor([self.batch_size]     ,	self.num_choices)

          _lowerCamelCase  :		Optional[Any]			    =    self.get_config()

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->str:


          """simple docstring"""


          return YosoConfig(
              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=_UpperCamelCase     ,	initializer_range=self.initializer_range     ,	)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.get_config()
          _lowerCamelCase  :		List[str]			    =    300
          return config



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Optional[Any]:


          """simple docstring"""


          (
              (
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				(
              _lowerCamelCase
          )    ,				
          )  :		Dict			    =    self.prepare_config_and_inputs()

          _lowerCamelCase  :		int			    =    True
          _lowerCamelCase  :		List[Any]			    =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          _lowerCamelCase  :		Any			    =    ids_tensor([self.batch_size, self.seq_length]     ,	vocab_size=2)

          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Optional[int])					->str:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    YosoModel(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Union[str, Any]			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase)
          _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase     ,	token_type_ids=_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    model(_UpperCamelCase)
          self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					Optional[int]     ,	)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    True
          _lowerCamelCase  :		str			    =    YosoModel(_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Optional[int]			    =    model(
              _UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	encoder_hidden_states=_UpperCamelCase     ,	encoder_attention_mask=_UpperCamelCase     ,	)
          _lowerCamelCase  :		Union[str, Any]			    =    model(
              _UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	encoder_hidden_states=_UpperCamelCase     ,	)
          _lowerCamelCase  :		List[str]			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase)
          self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    YosoForMaskedLM(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Tuple			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Union[str, Any])					->Any:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    YosoForQuestionAnswering(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		int			    =    model(
              _UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	start_positions=_UpperCamelCase     ,	end_positions=_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 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					str)					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    self.num_labels
          _lowerCamelCase  :		Tuple			    =    YosoForSequenceClassification(_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any]     ,	_UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					str)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    self.num_labels
          _lowerCamelCase  :		Union[str, Any]			    =    YosoForTokenClassification(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		str			    =    model(_UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any]     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Tuple)					->str:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.num_choices
          _lowerCamelCase  :		Tuple			    =    YosoForMultipleChoice(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		Optional[int]			    =    input_ids.unsqueeze(1).expand(-1     ,	self.num_choices     ,	-1).contiguous()
          _lowerCamelCase  :		Union[str, Any]			    =    token_type_ids.unsqueeze(1).expand(-1     ,	self.num_choices     ,	-1).contiguous()
          _lowerCamelCase  :		Optional[Any]			    =    input_mask.unsqueeze(1).expand(-1     ,	self.num_choices     ,	-1).contiguous()
          _lowerCamelCase  :		str			    =    model(
              _UpperCamelCase     ,	attention_mask=_UpperCamelCase     ,	token_type_ids=_UpperCamelCase     ,	labels=_UpperCamelCase     ,	)
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_choices))




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Union[str, Any]:


          """simple docstring"""


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





@require_torch
class 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase			,	unittest.TestCase		):





      '''simple docstring'''






      _snake_case     		=							(
          (
              YosoModel,
              YosoForMaskedLM,
              YosoForMultipleChoice,
              YosoForQuestionAnswering,
              YosoForSequenceClassification,
              YosoForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _snake_case     		=							False
      _snake_case     		=							False
      _snake_case     		=							False

      _snake_case     		=							()
      _snake_case     		=							(
          {
              'feature-extraction': YosoModel,
              'fill-mask': YosoForMaskedLM,
              'question-answering': YosoForQuestionAnswering,
              'text-classification': YosoForSequenceClassification,
              'token-classification': YosoForTokenClassification,
              'zero-shot': YosoForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    YosoModelTester(self)
          _lowerCamelCase  :		Optional[int]			    =    ConfigTester(self     ,	config_class=_UpperCamelCase     ,	hidden_size=37)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Optional[Any]:


          """simple docstring"""


          self.config_tester.run_common_tests()



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              _lowerCamelCase  :		List[str]			    =    type
              self.model_tester.create_and_check_model(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase)



      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->List[str]:


          """simple docstring"""


          for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCamelCase  :		Tuple			    =    YosoModel.from_pretrained(_UpperCamelCase)
              self.assertIsNotNone(_UpperCamelCase)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Union[str, Any]:


          """simple docstring"""


          return





@require_torch
class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    YosoModel.from_pretrained("""uw-madison/yoso-4096""")
          _lowerCamelCase  :		int			    =    torch.tensor([[0, 1, 2, 3, 4, 5]])

          with torch.no_grad():
              _lowerCamelCase  :		Any			    =    model(_UpperCamelCase)[0]

          _lowerCamelCase  :		List[Any]			    =    torch.Size((1, 6, 768))
          self.assertEqual(output.shape     ,	_UpperCamelCase)

          _lowerCamelCase  :		int			    =    torch.tensor(
              [[[-0.0_6_1_1, 0.1_2_4_2, 0.0_8_4_0], [0.0_2_8_0, -0.0_0_4_8, 0.1_1_2_5], [0.0_1_0_6, 0.0_2_2_6, 0.0_7_5_1]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))



      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->str:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096""")
          _lowerCamelCase  :		Tuple			    =    torch.tensor([[0, 1, 2, 3, 4, 5]])

          with torch.no_grad():
              _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase)[0]

          _lowerCamelCase  :		Union[str, Any]			    =    5_0265

          _lowerCamelCase  :		Any			    =    torch.Size((1, 6, vocab_size))
          self.assertEqual(output.shape     ,	_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    torch.tensor(
              [[[-2.1_3_1_3, -3.7_2_8_5, -2.2_4_0_7], [-2.7_0_4_7, -3.3_3_1_4, -2.6_4_0_8], [0.0_6_2_9, -2.5_1_6_6, -0.3_3_5_6]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))




      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096""")
          _lowerCamelCase  :		Any			    =    torch.arange(4096).unsqueeze(0)

          with torch.no_grad():
              _lowerCamelCase  :		Optional[Any]			    =    model(_UpperCamelCase)[0]

          _lowerCamelCase  :		Dict			    =    5_0265

          _lowerCamelCase  :		str			    =    torch.Size((1, 4096, vocab_size))
          self.assertEqual(output.shape     ,	_UpperCamelCase)

          _lowerCamelCase  :		int			    =    torch.tensor(
              [[[-2.3_9_1_4, -4.3_7_4_2, -5.0_9_5_6], [-4.0_9_8_8, -4.2_3_8_4, -7.0_4_0_6], [-3.1_4_2_7, -3.7_1_9_2, -6.6_8_0_0]]])

          self.assertTrue(torch.allclose(output[:, :3, :3]     ,	_UpperCamelCase     ,	atol=1E-4))





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


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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							None







def     A__	(       __A ,			__A=0.999 ,			__A="cosine" ,			):







    '''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}"""						)

    _lowerCamelCase  :		int			    =    []
    for i in range(__A						):
        _lowerCamelCase  :		Optional[int]			    =    i / num_diffusion_timesteps
        _lowerCamelCase  :		int			    =    (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 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							[e.name for e in KarrasDiffusionSchedulers]
      _snake_case     		=							1



      @register_to_config
      def __init__(       self  :					Tuple     ,	_UpperCamelCase  :					int = 1000     ,	_UpperCamelCase  :					float = 0.0_0_0_1     ,	_UpperCamelCase  :					float = 0.0_2     ,	_UpperCamelCase  :					str = "linear"     ,	_UpperCamelCase  :					Optional[Union[np.ndarray, List[float]]] = None     ,	_UpperCamelCase  :					str = "epsilon"     ,	_UpperCamelCase  :					str = "linspace"     ,	_UpperCamelCase  :					int = 0     ,	)					->Optional[int]:


          """simple docstring"""


          if trained_betas is not None:
              _lowerCamelCase  :		List[str]			    =    torch.tensor(_UpperCamelCase     ,	dtype=torch.floataa)
          elif beta_schedule == "linear":
              _lowerCamelCase  :		List[str]			    =    torch.linspace(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	dtype=torch.floataa)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              _lowerCamelCase  :		int			    =    (
                  torch.linspace(beta_start**0.5     ,	beta_end**0.5     ,	_UpperCamelCase     ,	dtype=torch.floataa) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              _lowerCamelCase  :		Any			    =    betas_for_alpha_bar(_UpperCamelCase)
          else:
              raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

          _lowerCamelCase  :		Tuple			    =    1.0 - self.betas
          _lowerCamelCase  :		int			    =    torch.cumprod(self.alphas     ,	dim=0)

          _lowerCamelCase  :		Tuple			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Dict			    =    np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Tuple			    =    torch.from_numpy(_UpperCamelCase)

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



      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Dict:


          """simple docstring"""


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

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor])					->torch.FloatTensor:


          """simple docstring"""


          if isinstance(_UpperCamelCase     ,	torch.Tensor):
              _lowerCamelCase  :		Optional[int]			    =    timestep.to(self.timesteps.device)
          _lowerCamelCase  :		Tuple			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		str			    =    self.sigmas[step_index]
          _lowerCamelCase  :		List[str]			    =    sample / ((sigma**2 + 1) ** 0.5)
          _lowerCamelCase  :		str			    =    True
          return sample



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Union[str, torch.device] = None)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    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":
              _lowerCamelCase  :		Any			    =    np.linspace(0     ,	self.config.num_train_timesteps - 1     ,	_UpperCamelCase     ,	dtype=_UpperCamelCase)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              _lowerCamelCase  :		Tuple			    =    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
              _lowerCamelCase  :		List[Any]			    =    (np.arange(0     ,	_UpperCamelCase) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              _lowerCamelCase  :		List[str]			    =    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
              _lowerCamelCase  :		str			    =    (np.arange(self.config.num_train_timesteps     ,	0     ,	-step_ratio)).round().copy().astype(_UpperCamelCase)
              timesteps -= 1
          else:
              raise ValueError(
                  F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

          _lowerCamelCase  :		Tuple			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Union[str, Any]			    =    np.interp(_UpperCamelCase     ,	np.arange(0     ,	len(_UpperCamelCase))     ,	_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    np.concatenate([sigmas, [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Dict			    =    torch.from_numpy(_UpperCamelCase).to(device=_UpperCamelCase)
          if str(_UpperCamelCase).startswith("""mps"""):
              # mps does not support float64
              _lowerCamelCase  :		List[str]			    =    torch.from_numpy(_UpperCamelCase).to(_UpperCamelCase     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		Any			    =    torch.from_numpy(_UpperCamelCase).to(device=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Optional[torch.Generator] = None     ,	_UpperCamelCase  :					bool = True     ,	)					->Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:


          """simple docstring"""


          if (
              isinstance(_UpperCamelCase     ,	_UpperCamelCase)
              or isinstance(_UpperCamelCase     ,	torch.IntTensor)
              or isinstance(_UpperCamelCase     ,	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(_UpperCamelCase     ,	torch.Tensor):
              _lowerCamelCase  :		Optional[Any]			    =    timestep.to(self.timesteps.device)

          _lowerCamelCase  :		Optional[int]			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		Union[str, Any]			    =    self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              _lowerCamelCase  :		int			    =    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              _lowerCamelCase  :		Optional[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`""")

          _lowerCamelCase  :		Tuple			    =    self.sigmas[step_index]
          _lowerCamelCase  :		int			    =    self.sigmas[step_index + 1]
          _lowerCamelCase  :		Tuple			    =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          _lowerCamelCase  :		List[Any]			    =    (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          _lowerCamelCase  :		List[Any]			    =    (sample - pred_original_sample) / sigma

          _lowerCamelCase  :		Tuple			    =    sigma_down - sigma

          _lowerCamelCase  :		Optional[int]			    =    sample + derivative * dt

          _lowerCamelCase  :		int			    =    model_output.device
          _lowerCamelCase  :		List[Any]			    =    randn_tensor(model_output.shape     ,	dtype=model_output.dtype     ,	device=_UpperCamelCase     ,	generator=_UpperCamelCase)

          _lowerCamelCase  :		List[Any]			    =    prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=_UpperCamelCase     ,	pred_original_sample=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	)					->torch.FloatTensor:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.sigmas.to(device=original_samples.device     ,	dtype=original_samples.dtype)
          if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase):
              # mps does not support float64
              _lowerCamelCase  :		str			    =    self.timesteps.to(original_samples.device     ,	dtype=torch.floataa)
              _lowerCamelCase  :		Union[str, Any]			    =    timesteps.to(original_samples.device     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		Union[str, Any]			    =    self.timesteps.to(original_samples.device)
              _lowerCamelCase  :		List[str]			    =    timesteps.to(original_samples.device)

          _lowerCamelCase  :		Optional[int]			    =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          _lowerCamelCase  :		List[Any]			    =    sigmas[step_indices].flatten()
          while len(sigma.shape) < len(original_samples.shape):
              _lowerCamelCase  :		Optional[Any]			    =    sigma.unsqueeze(-1)

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




      def __len__(       self  :					Dict)					->str:


          """simple docstring"""


          return self.config.num_train_timesteps




