
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[int]=13					, SCREAMING_SNAKE_CASE__			:			int=7					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=True					, SCREAMING_SNAKE_CASE__			:			List[str]=True					, SCREAMING_SNAKE_CASE__			:			Optional[int]=False					, SCREAMING_SNAKE_CASE__			:			str=True					, SCREAMING_SNAKE_CASE__			:			int=99					, SCREAMING_SNAKE_CASE__			:			Dict=32					, SCREAMING_SNAKE_CASE__			:			Optional[int]=5					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=4					, SCREAMING_SNAKE_CASE__			:			List[str]=37					, SCREAMING_SNAKE_CASE__			:			Tuple="gelu"					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			Dict=0.1					, SCREAMING_SNAKE_CASE__			:			Any=5_12					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=16					, SCREAMING_SNAKE_CASE__			:			Optional[int]=2					, SCREAMING_SNAKE_CASE__			:			Tuple=0.02					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=3					, SCREAMING_SNAKE_CASE__			:			Optional[int]=4					, SCREAMING_SNAKE_CASE__			:			Tuple=None					, ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """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						    =					self.get_config()

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

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      return OpenLlamaConfig(
          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=SCREAMING_SNAKE_CASE__					, initializer_range=self.initializer_range					, use_stable_embedding=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					OpenLlamaModel(config=SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)   )

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			Any					, ):






      """simple docstring"""



      UpperCamelCase						    =					True
      UpperCamelCase						    =					OpenLlamaModel(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(
          SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, encoder_hidden_states=SCREAMING_SNAKE_CASE__					, encoder_attention_mask=SCREAMING_SNAKE_CASE__					, )
      UpperCamelCase						    =					model(
          SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, encoder_hidden_states=SCREAMING_SNAKE_CASE__					, )
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)   )

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, ):






      """simple docstring"""



      UpperCamelCase						    =					OpenLlamaForCausalLM(config=SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, labels=SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)   )

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, ):






      """simple docstring"""



      UpperCamelCase						    =					True
      UpperCamelCase						    =					True
      UpperCamelCase						    =					OpenLlamaForCausalLM(config=SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()

      # first forward pass
      UpperCamelCase						    =					model(
          SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, encoder_hidden_states=SCREAMING_SNAKE_CASE__					, encoder_attention_mask=SCREAMING_SNAKE_CASE__					, use_cache=SCREAMING_SNAKE_CASE__					, )
      UpperCamelCase						    =					outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      UpperCamelCase						    =					ids_tensor((self.batch_size, 3)					, config.vocab_size   )
      UpperCamelCase						    =					ids_tensor((self.batch_size, 3)					, vocab_size=2   )

      # append to next input_ids and
      UpperCamelCase						    =					torch.cat([input_ids, next_tokens]					, dim=-1   )
      UpperCamelCase						    =					torch.cat([input_mask, next_mask]					, dim=-1   )

      UpperCamelCase						    =					model(
          SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, encoder_hidden_states=SCREAMING_SNAKE_CASE__					, encoder_attention_mask=SCREAMING_SNAKE_CASE__					, output_hidden_states=SCREAMING_SNAKE_CASE__					, )['hidden_states'][0]
      UpperCamelCase						    =					model(
          SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, encoder_hidden_states=SCREAMING_SNAKE_CASE__					, encoder_attention_mask=SCREAMING_SNAKE_CASE__					, past_key_values=SCREAMING_SNAKE_CASE__					, output_hidden_states=SCREAMING_SNAKE_CASE__					, )['hidden_states'][0]

      # select random slice
      UpperCamelCase						    =					ids_tensor((1,)					, output_from_past.shape[-1]   ).item()
      UpperCamelCase						    =					output_from_no_past[:, -3:, random_slice_idx].detach()
      UpperCamelCase						    =					output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, atol=1e-3   )   )






 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



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




@require_torch
class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__			,	__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 SCREAMING_SNAKE_CASE_				:					Dict       						=(OpenLlamaForCausalLM,) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE_				:					str       						=(
     {
         "feature-extraction": OpenLlamaModel,
         "text-classification": OpenLlamaForSequenceClassification,
         "text-generation": OpenLlamaForCausalLM,
         "zero-shot": OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=False
 SCREAMING_SNAKE_CASE_				:					Any       						=False

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					OpenLlamaModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=SCREAMING_SNAKE_CASE__					, hidden_size=37   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      self.config_tester.run_common_tests()

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



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

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






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           UpperCamelCase						    =					type
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					3
      UpperCamelCase						    =					input_dict['input_ids']
      UpperCamelCase						    =					input_ids.ne(1   ).to(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					ids_tensor([self.model_tester.batch_size]					, self.model_tester.type_sequence_label_size   )
      UpperCamelCase						    =					OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, labels=SCREAMING_SNAKE_CASE__   )
      self.assertEqual(result.logits.shape					, (self.model_tester.batch_size, self.model_tester.num_labels)   )

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					3
      UpperCamelCase						    =					'single_label_classification'
      UpperCamelCase						    =					input_dict['input_ids']
      UpperCamelCase						    =					input_ids.ne(1   ).to(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					ids_tensor([self.model_tester.batch_size]					, self.model_tester.type_sequence_label_size   )
      UpperCamelCase						    =					OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, labels=SCREAMING_SNAKE_CASE__   )
      self.assertEqual(result.logits.shape					, (self.model_tester.batch_size, self.model_tester.num_labels)   )

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					3
      UpperCamelCase						    =					'multi_label_classification'
      UpperCamelCase						    =					input_dict['input_ids']
      UpperCamelCase						    =					input_ids.ne(1   ).to(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					, self.model_tester.type_sequence_label_size   ).to(torch.float   )
      UpperCamelCase						    =					OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, attention_mask=SCREAMING_SNAKE_CASE__					, labels=SCREAMING_SNAKE_CASE__   )
      self.assertEqual(result.logits.shape					, (self.model_tester.batch_size, self.model_tester.num_labels)   )

 @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'   )
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      pass






 @parameterized.expand([('linear',), ('dynamic',)]   )
 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					ids_tensor([1, 10]					, config.vocab_size   )
      UpperCamelCase						    =					ids_tensor([1, int(config.max_position_embeddings * 1.5   )]					, config.vocab_size   )

      set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
      UpperCamelCase						    =					OpenLlamaModel(SCREAMING_SNAKE_CASE__   )
      original_model.to(SCREAMING_SNAKE_CASE__   )
      original_model.eval()
      UpperCamelCase						    =					original_model(SCREAMING_SNAKE_CASE__   ).last_hidden_state
      UpperCamelCase						    =					original_model(SCREAMING_SNAKE_CASE__   ).last_hidden_state

      set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
      UpperCamelCase						    =					{'type': scaling_type, 'factor': 10.0}
      UpperCamelCase						    =					OpenLlamaModel(SCREAMING_SNAKE_CASE__   )
      scaled_model.to(SCREAMING_SNAKE_CASE__   )
      scaled_model.eval()
      UpperCamelCase						    =					scaled_model(SCREAMING_SNAKE_CASE__   ).last_hidden_state
      UpperCamelCase						    =					scaled_model(SCREAMING_SNAKE_CASE__   ).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
           self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, atol=1e-5   )   )
      else:
           self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, atol=1e-5   )   )

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, atol=1e-5   )   )




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=(DEISMultistepScheduler,)
 SCREAMING_SNAKE_CASE_				:					List[str]       						=(("num_inference_steps", 25),)

 def      __lowerCAmelCase  (						self			:			Tuple					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
      }

      config.update(**SCREAMING_SNAKE_CASE__   )
      return config

 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			Dict=0					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           # copy over dummy past residuals
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
                # copy over dummy past residuals
                UpperCamelCase						    =					dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase    ,		UpperCamelCase						    =					sample, sample
           for t in range(SCREAMING_SNAKE_CASE__					, time_step + scheduler.config.solver_order + 1   ):
                UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
                UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

                assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0					, **SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

           # copy over dummy past residuals (must be after setting timesteps)
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

                # copy over dummy past residual (must be after setting timesteps)
                UpperCamelCase						    =					dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

           assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      if scheduler is None:
           UpperCamelCase						    =					self.scheduler_classes[0]
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

      for i, t in enumerate(scheduler.timesteps   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).prev_sample

      return sample

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )

      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self.dummy_sample
           UpperCamelCase						    =					0.1 * sample

           if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                UpperCamelCase						    =					num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           UpperCamelCase						    =					scheduler.timesteps[5]
           UpperCamelCase						    =					scheduler.timesteps[6]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

           self.assertEqual(output_a.shape					, sample.shape   )
           self.assertEqual(output_a.shape					, output_a.shape   )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					DEISMultistepScheduler(**self.get_scheduler_config()   )
      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

      UpperCamelCase						    =					DPMSolverSinglestepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					DPMSolverMultistepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					UniPCMultistepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					DEISMultistepScheduler.from_config(scheduler.config   )

      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

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






      """simple docstring"""



      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__   )
      for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
                for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, sample_max_value=SCREAMING_SNAKE_CASE__					, algorithm_type='deis'					, solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
                for order in [1, 2, 3]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          UpperCamelCase						    =					self.full_loop(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          assert not torch.isnan(SCREAMING_SNAKE_CASE__   ).any(), "Samples have nan numbers"

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )
      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
           self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__					, time_step=0   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop()
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop(prediction_type='v_prediction'   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.091   ) < 1e-3






 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__					, dynamic_thresholding_ratio=0   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter.half()
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

      for i, t in enumerate(scheduler.timesteps   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).prev_sample

      assert sample.dtype == torch.floataa


