


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class   A__					(    A__	):







    """simple docstring"""

    __magic_name__			  =		(UniPCMultistepScheduler,)
    __magic_name__			  =		(('num_inference_steps', 25),)
    def 		a_			(       self			,					**__snake_case    ):
         snake_case		 =				{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''solver_type''': '''bh2''',
         }

         config.update(**__snake_case    )
         return config
    def 		a_			(       self			,					__snake_case=0			,					**__snake_case    ):
         snake_case		 =				dict(self.forward_default_kwargs    )
         snake_case		 =				kwargs.pop('''num_inference_steps'''			,					__snake_case    )
         snake_case		 =				self.dummy_sample
         snake_case		 =				0.1 * sample
         snake_case		 =				[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

                   assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"
    def 		a_			(       self			,					__snake_case=0			,					**__snake_case    ):
         snake_case		 =				dict(self.forward_default_kwargs    )
         snake_case		 =				kwargs.pop('''num_inference_steps'''			,					__snake_case    )
         snake_case		 =				self.dummy_sample
         snake_case		 =				0.1 * sample
         snake_case		 =				[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
              snake_case		 =				self.get_scheduler_config()
              snake_case		 =				scheduler_class(**__snake_case    )
              scheduler.set_timesteps(__snake_case    )

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

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

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

              snake_case		 =				scheduler.step(__snake_case			,					__snake_case			,					__snake_case			,					**__snake_case    ).prev_sample
              snake_case		 =				new_scheduler.step(__snake_case			,					__snake_case			,					__snake_case			,					**__snake_case    ).prev_sample

              assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"
    def 		a_			(       self			,					__snake_case=None			,					**__snake_case    ):
         if scheduler is None:
              snake_case		 =				self.scheduler_classes[0]
              snake_case		 =				self.get_scheduler_config(**__snake_case    )
              snake_case		 =				scheduler_class(**__snake_case    )

         snake_case		 =				self.scheduler_classes[0]
         snake_case		 =				self.get_scheduler_config(**__snake_case    )
         snake_case		 =				scheduler_class(**__snake_case    )

         snake_case		 =				1_0
         snake_case		 =				self.dummy_model()
         snake_case		 =				self.dummy_sample_deter
         scheduler.set_timesteps(__snake_case    )

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

         return sample
    def 		a_			(       self    ):
         snake_case		 =				dict(self.forward_default_kwargs    )

         snake_case		 =				kwargs.pop('''num_inference_steps'''			,					__snake_case    )

         for scheduler_class in self.scheduler_classes:
              snake_case		 =				self.get_scheduler_config()
              snake_case		 =				scheduler_class(**__snake_case    )

              snake_case		 =				self.dummy_sample
              snake_case		 =				0.1 * sample

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

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

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

              snake_case		 =				scheduler.step(__snake_case			,					__snake_case			,					__snake_case			,					**__snake_case    ).prev_sample
              snake_case		 =				scheduler.step(__snake_case			,					__snake_case			,					__snake_case			,					**__snake_case    ).prev_sample

              self.assertEqual(output_a.shape			,					sample.shape    )
              self.assertEqual(output_a.shape			,					output_a.shape    )
    def 		a_			(       self    ):
         snake_case		 =				UniPCMultistepScheduler(**self.get_scheduler_config()    )
         snake_case		 =				self.full_loop(scheduler=__snake_case    )
         snake_case		 =				torch.mean(torch.abs(__snake_case    )    )

         assert abs(result_mean.item() - 0.2464    ) < 1E-3

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

         snake_case		 =				self.full_loop(scheduler=__snake_case    )
         snake_case		 =				torch.mean(torch.abs(__snake_case    )    )

         assert abs(result_mean.item() - 0.2464    ) < 1E-3
    def 		a_			(       self    ):
         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=__snake_case    )
    def 		a_			(       self    ):
         self.check_over_configs(thresholding=__snake_case    )
         for order in [1, 2, 3]:
              for solver_type in ["bh1", "bh2"]:
                   for threshold in [0.5, 1.0, 2.0]:
                        for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 thresholding=__snake_case			,					prediction_type=__snake_case			,					sample_max_value=__snake_case			,					solver_order=__snake_case			,					solver_type=__snake_case			,					)
    def 		a_			(       self    ):
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__snake_case    )
    def 		a_			(       self    ):
         for solver_type in ["bh1", "bh2"]:
              for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            solver_order=__snake_case			,					solver_type=__snake_case			,					prediction_type=__snake_case			,					)
                        snake_case		 =				self.full_loop(
                            solver_order=__snake_case			,					solver_type=__snake_case			,					prediction_type=__snake_case			,					)
                        assert not torch.isnan(__snake_case    ).any(), "Samples have nan numbers"
    def 		a_			(       self    ):
         self.check_over_configs(lower_order_final=__snake_case    )
         self.check_over_configs(lower_order_final=__snake_case    )
    def 		a_			(       self    ):
         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
              self.check_over_forward(num_inference_steps=__snake_case			,					time_step=0    )
    def 		a_			(       self    ):
         snake_case		 =				self.full_loop()
         snake_case		 =				torch.mean(torch.abs(__snake_case    )    )

         assert abs(result_mean.item() - 0.2464    ) < 1E-3
    def 		a_			(       self    ):
         snake_case		 =				self.full_loop(prediction_type='''v_prediction'''    )
         snake_case		 =				torch.mean(torch.abs(__snake_case    )    )

         assert abs(result_mean.item() - 0.1014    ) < 1E-3
    def 		a_			(       self    ):
         snake_case		 =				self.scheduler_classes[0]
         snake_case		 =				self.get_scheduler_config(thresholding=__snake_case			,					dynamic_thresholding_ratio=0    )
         snake_case		 =				scheduler_class(**__snake_case    )

         snake_case		 =				1_0
         snake_case		 =				self.dummy_model()
         snake_case		 =				self.dummy_sample_deter.half()
         scheduler.set_timesteps(__snake_case    )

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

         assert sample.dtype == torch.floataa




    def 		a_			(       self			,					**__snake_case    ):
         for scheduler_class in self.scheduler_classes:
              snake_case		 =				self.get_scheduler_config(**__snake_case    )
              snake_case		 =				scheduler_class(**__snake_case    )

              scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
              assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps












import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, 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 (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)
class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str=14,  lowerCamelCase						:    List[str]=7,  lowerCamelCase						:    Dict=True,  lowerCamelCase						:    List[str]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    List[str]=99,  lowerCamelCase						:    Optional[Any]=32,  lowerCamelCase						:    Optional[int]=5,  lowerCamelCase						:    List[Any]=4,  lowerCamelCase						:    List[Any]=37,  lowerCamelCase						:    List[str]="gelu",  lowerCamelCase						:    Any=0.1,  lowerCamelCase						:    Optional[Any]=0.1,  lowerCamelCase						:    List[Any]=512,  lowerCamelCase						:    List[str]=16,  lowerCamelCase						:    Dict=2,  lowerCamelCase						:    Union[str, Any]=0.02,  lowerCamelCase						:    Optional[int]=3,  lowerCamelCase						:    Dict=4,  lowerCamelCase						:    List[Any]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			seq_length
								lowercase__    =			is_training
								lowercase__    =			use_token_type_ids
								lowercase__    =			use_input_mask
								lowercase__    =			use_labels
								lowercase__    =			use_mc_token_ids
								lowercase__    =			vocab_size
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			max_position_embeddings
								lowercase__    =			type_vocab_size
								lowercase__    =			type_sequence_label_size
								lowercase__    =			initializer_range
								lowercase__    =			num_labels
								lowercase__    =			num_choices
								lowercase__    =			scope
								lowercase__    =			self.vocab_size - 1




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

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

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

								lowercase__    =			None
								if self.use_mc_token_ids:
												lowercase__    =			ids_tensor([self.batch_size, self.num_choices],  self.seq_length  )

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

								lowercase__    =			self.get_config()

								lowercase__    =			ids_tensor([self.num_hidden_layers, self.num_attention_heads],  2  )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								return CTRLConfig(
								    vocab_size=self.vocab_size,  n_embd=self.hidden_size,  n_layer=self.num_hidden_layers,  n_head=self.num_attention_heads,  n_positions=self.max_position_embeddings,  pad_token_id=self.pad_token_id,  )




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Union[str, Any],  *lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			CTRLModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								model(lowerCamelCase,  token_type_ids=lowerCamelCase,  head_mask=lowerCamelCase  )
								model(lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )
								self.parent.assertEqual(len(result.past_key_values  ),  config.n_layer  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int],  *lowerCamelCase						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.loss.shape,  ()  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()

								(
								    (
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						
								)    =			config_and_inputs

								lowercase__    =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

								return config, inputs_dict




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    Any,  *lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			CTRLForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
				lowercase__											=       (CTRLLMHeadModel,) if is_torch_available() else ()
				lowercase__											=       (
				    {
				        """feature-extraction""": CTRLModel,
				        """text-classification""": CTRLForSequenceClassification,
				        """text-generation""": CTRLLMHeadModel,
				        """zero-shot""": CTRLForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__											=       True
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
												# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
												# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
												# config could not be created.
												return True

								return False




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  n_embd=37  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCamelCase  )




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								pass




				@slow
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			CTRLModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )




				@unittest.skip('''The model doesn\'t support left padding'''  )  # and it's not used enough to be worth fixing :)
				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								pass



@require_torch
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()




				@slow
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel.from_pretrained('''ctrl'''  )
								model.to(lowerCamelCase  )
								lowercase__    =			torch.tensor(
								    [[11_859, 0, 1_611, 8]],  dtype=torch.long,  device=lowerCamelCase  )  # Legal the president is
								lowercase__    =			[
								    11_859,
								    0,
								    1_611,
								    8,
								    5,
								    150,
								    26_449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2_595,
								    48,
								    20_740,
								    246_533,
								    246_533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								lowercase__    =			model.generate(lowerCamelCase,  do_sample=lowerCamelCase  )
								self.assertListEqual(output_ids[0].tolist(),  lowerCamelCase  )




