

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  (DPMSolverMultistepScheduler,)
   UpperCamelCase       =  (("""num_inference_steps""", 2_5),)



   def  __magic_name__					(							self							:  Union[str, Any],	**__A							:  int		):
     UpperCAmelCase :							Any								=				{
         '''num_train_timesteps''': 1_0_0_0,
         '''beta_start''': 0.0_0_0_1,
         '''beta_end''': 0.0_2,
         '''beta_schedule''': '''linear''',
         '''solver_order''': 2,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
         '''sample_max_value''': 1.0,
         '''algorithm_type''': '''dpmsolver++''',
         '''solver_type''': '''midpoint''',
         '''lower_order_final''': False,
         '''lambda_min_clipped''': -float('''inf'''		),
         '''variance_type''': None,
     }

     config.update(**__A		)
     return config



   def  __magic_name__					(							self							:  Union[str, Any],	__A							:  Optional[Any]=0,	**__A							:  str		):
     UpperCAmelCase :							Optional[Any]								=				dict(self.forward_default_kwargs		)
     UpperCAmelCase :							int								=				kwargs.pop('''num_inference_steps''',	__A		)
     UpperCAmelCase :							Optional[Any]								=				self.dummy_sample
     UpperCAmelCase :							Any								=				0.1 * sample
     UpperCAmelCase :							Any								=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
       UpperCAmelCase :							Dict								=				self.get_scheduler_config(**__A		)
       UpperCAmelCase :							List[Any]								=				scheduler_class(**__A		)
       scheduler.set_timesteps(__A		)
       # copy over dummy past residuals
       UpperCAmelCase :							str								=				dummy_past_residuals[: scheduler.config.solver_order]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(__A		)
         UpperCAmelCase :							int								=				scheduler_class.from_pretrained(__A		)
         new_scheduler.set_timesteps(__A		)
         # copy over dummy past residuals
         UpperCAmelCase :							List[str]								=				dummy_past_residuals[: new_scheduler.config.solver_order]

       UpperCAmelCase				,		UpperCAmelCase :							Any								=				sample, sample
       for t in range(__A,	time_step + scheduler.config.solver_order + 1		):
         UpperCAmelCase :							Optional[Any]								=				scheduler.step(__A,	__A,	__A,	**__A		).prev_sample
         UpperCAmelCase :							int								=				new_scheduler.step(__A,	__A,	__A,	**__A		).prev_sample

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



   def  __magic_name__					(							self							:  Dict		):
     pass



   def  __magic_name__					(							self							:  List[str],	__A							:  List[str]=0,	**__A							:  List[str]		):
     UpperCAmelCase :							Any								=				dict(self.forward_default_kwargs		)
     UpperCAmelCase :							str								=				kwargs.pop('''num_inference_steps''',	__A		)
     UpperCAmelCase :							Optional[int]								=				self.dummy_sample
     UpperCAmelCase :							Tuple								=				0.1 * sample
     UpperCAmelCase :							Optional[Any]								=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
       UpperCAmelCase :							List[str]								=				self.get_scheduler_config()
       UpperCAmelCase :							Optional[Any]								=				scheduler_class(**__A		)
       scheduler.set_timesteps(__A		)

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

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

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

       UpperCAmelCase :							Any								=				scheduler.step(__A,	__A,	__A,	**__A		).prev_sample
       UpperCAmelCase :							int								=				new_scheduler.step(__A,	__A,	__A,	**__A		).prev_sample

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



   def  __magic_name__					(							self							:  List[Any],	__A							:  Any=None,	**__A							:  str		):
     if scheduler is None:
       UpperCAmelCase :							Optional[Any]								=				self.scheduler_classes[0]
       UpperCAmelCase :							Any								=				self.get_scheduler_config(**__A		)
       UpperCAmelCase :							Dict								=				scheduler_class(**__A		)

     UpperCAmelCase :							Optional[Any]								=				1_0
     UpperCAmelCase :							Any								=				self.dummy_model()
     UpperCAmelCase :							Optional[int]								=				self.dummy_sample_deter
     scheduler.set_timesteps(__A		)

     for i, t in enumerate(scheduler.timesteps		):
       UpperCAmelCase :							Union[str, Any]								=				model(__A,	__A		)
       UpperCAmelCase :							Optional[Any]								=				scheduler.step(__A,	__A,	__A		).prev_sample

     return sample



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							Optional[Any]								=				dict(self.forward_default_kwargs		)

     UpperCAmelCase :							Tuple								=				kwargs.pop('''num_inference_steps''',	__A		)

     for scheduler_class in self.scheduler_classes:
       UpperCAmelCase :							List[str]								=				self.get_scheduler_config()
       UpperCAmelCase :							List[str]								=				scheduler_class(**__A		)

       UpperCAmelCase :							Optional[int]								=				self.dummy_sample
       UpperCAmelCase :							Tuple								=				0.1 * sample

       if num_inference_steps is not None and hasattr(__A,	'''set_timesteps'''		):
         scheduler.set_timesteps(__A		)
       elif num_inference_steps is not None and not hasattr(__A,	'''set_timesteps'''		):
         UpperCAmelCase :							int								=				num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       UpperCAmelCase :							int								=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
       UpperCAmelCase :							int								=				dummy_past_residuals[: scheduler.config.solver_order]

       UpperCAmelCase :							Optional[int]								=				scheduler.timesteps[5]
       UpperCAmelCase :							Dict								=				scheduler.timesteps[6]

       UpperCAmelCase :							Tuple								=				scheduler.step(__A,	__A,	__A,	**__A		).prev_sample
       UpperCAmelCase :							Dict								=				scheduler.step(__A,	__A,	__A,	**__A		).prev_sample

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



   def  __magic_name__					(							self							:  List[Any]		):
     for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__A		)



   def  __magic_name__					(							self							:  Optional[Any]		):
     self.check_over_configs(thresholding=__A		)
     for order in [1, 2, 3]:
       for solver_type in ["midpoint", "heun"]:
         for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(
                 thresholding=__A,	prediction_type=__A,	sample_max_value=__A,	algorithm_type='''dpmsolver++''',	solver_order=__A,	solver_type=__A,	)



   def  __magic_name__					(							self							:  List[str]		):
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=__A		)



   def  __magic_name__					(							self							:  Any		):
     for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
       for solver_type in ["midpoint", "heun"]:
         for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
             if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
               if order == 3:
                 continue
             else:
               self.check_over_configs(
                   solver_order=__A,	solver_type=__A,	prediction_type=__A,	algorithm_type=__A,	)
             UpperCAmelCase :							Optional[int]								=				self.full_loop(
                 solver_order=__A,	solver_type=__A,	prediction_type=__A,	algorithm_type=__A,	)
             assert not torch.isnan(__A		).any(), "Samples have nan numbers"



   def  __magic_name__					(							self							:  Union[str, Any]		):
     self.check_over_configs(lower_order_final=__A		)
     self.check_over_configs(lower_order_final=__A		)



   def  __magic_name__					(							self							:  Optional[Any]		):
     self.check_over_configs(lambda_min_clipped=-float('''inf'''		)		)
     self.check_over_configs(lambda_min_clipped=-5.1		)



   def  __magic_name__					(							self							:  Union[str, Any]		):
     self.check_over_configs(variance_type=__A		)
     self.check_over_configs(variance_type='''learned_range'''		)



   def  __magic_name__					(							self							:  List[str]		):
     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=__A,	time_step=0		)



   def  __magic_name__					(							self							:  Dict		):
     UpperCAmelCase :							List[Any]								=				self.full_loop()
     UpperCAmelCase :							Optional[int]								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3



   def  __magic_name__					(							self							:  Optional[Any]		):
     UpperCAmelCase :							List[str]								=				self.full_loop(thresholding=__A,	dynamic_thresholding_ratio=0.8_7,	sample_max_value=0.5		)
     UpperCAmelCase :							Optional[int]								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_mean.item() - 1.1_3_6_4		) < 1E-3



   def  __magic_name__					(							self							:  List[str]		):
     UpperCAmelCase :							str								=				self.full_loop(prediction_type='''v_prediction'''		)
     UpperCAmelCase :							str								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_mean.item() - 0.2_2_5_1		) < 1E-3



   def  __magic_name__					(							self							:  Dict		):
     UpperCAmelCase :							Tuple								=				self.full_loop(prediction_type='''v_prediction''',	use_karras_sigmas=__A		)
     UpperCAmelCase :							Optional[Any]								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_mean.item() - 0.2_0_9_6		) < 1E-3



   def  __magic_name__					(							self							:  Any		):
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     UpperCAmelCase :							Optional[Any]								=				DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
     UpperCAmelCase :							Union[str, Any]								=				self.full_loop(scheduler=__A		)
     UpperCAmelCase :							Any								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3

     UpperCAmelCase :							int								=				DPMSolverSinglestepScheduler.from_config(scheduler.config		)
     UpperCAmelCase :							Union[str, Any]								=				UniPCMultistepScheduler.from_config(scheduler.config		)
     UpperCAmelCase :							Tuple								=				DEISMultistepScheduler.from_config(scheduler.config		)
     UpperCAmelCase :							Optional[int]								=				DPMSolverMultistepScheduler.from_config(scheduler.config		)

     UpperCAmelCase :							List[str]								=				self.full_loop(scheduler=__A		)
     UpperCAmelCase :							Dict								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3



   def  __magic_name__					(							self							:  Tuple		):
     UpperCAmelCase :							Optional[int]								=				self.scheduler_classes[0]
     UpperCAmelCase :							List[Any]								=				self.get_scheduler_config(thresholding=__A,	dynamic_thresholding_ratio=0		)
     UpperCAmelCase :							Any								=				scheduler_class(**__A		)

     UpperCAmelCase :							Union[str, Any]								=				1_0
     UpperCAmelCase :							int								=				self.dummy_model()
     UpperCAmelCase :							Tuple								=				self.dummy_sample_deter.half()
     scheduler.set_timesteps(__A		)

     for i, t in enumerate(scheduler.timesteps		):
       UpperCAmelCase :							Dict								=				model(__A,	__A		)
       UpperCAmelCase :							Tuple								=				scheduler.step(__A,	__A,	__A		).prev_sample

     assert sample.dtype == torch.floataa





   def  __magic_name__					(							self							:  Any,	**__A							:  Optional[Any]		):
     for scheduler_class in self.scheduler_classes:
       UpperCAmelCase :							Dict								=				self.get_scheduler_config(**__A		)
       UpperCAmelCase :							str								=				scheduler_class(**__A		)

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








import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class       __UpperCAmelCase  ( unittest.TestCase		):



   def  __magic_name__					(							self							:  Dict		):
     UpperCAmelCase :							Tuple								=				'''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''		)
     UpperCAmelCase :							List[str]								=				dict(zip(__A,	range(len(__A		)		)		)		)

     UpperCAmelCase :							int								=				{
         '''pad_token''': '''<pad>''',
         '''unk_token''': '''<unk>''',
         '''bos_token''': '''<s>''',
         '''eos_token''': '''</s>''',
     }
     UpperCAmelCase :							str								=				{
         '''feature_size''': 1,
         '''padding_value''': 0.0,
         '''sampling_rate''': 1_6_0_0_0,
         '''return_attention_mask''': False,
         '''do_normalize''': True,
     }

     UpperCAmelCase :							List[Any]								=				tempfile.mkdtemp()
     UpperCAmelCase :							str								=				os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['''vocab_file''']		)
     UpperCAmelCase :							Tuple								=				os.path.join(self.tmpdirname,	__A		)
     with open(self.vocab_file,	'''w''',	encoding='''utf-8'''		) as fp:
       fp.write(json.dumps(__A		) + '''\n'''		)

     with open(self.feature_extraction_file,	'''w''',	encoding='''utf-8'''		) as fp:
       fp.write(json.dumps(__A		) + '''\n'''		)



   def  __magic_name__					(							self							:  List[Any],	**__A							:  List[Any]		):
     UpperCAmelCase :							str								=				self.add_kwargs_tokens_map.copy()
     kwargs.update(__A		)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,	**__A		)



   def  __magic_name__					(							self							:  List[Any],	**__A							:  Union[str, Any]		):
     return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,	**__A		)



   def  __magic_name__					(							self							:  Any		):
     shutil.rmtree(self.tmpdirname		)



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							Any								=				self.get_tokenizer()
     UpperCAmelCase :							Optional[int]								=				self.get_feature_extractor()

     UpperCAmelCase :							Tuple								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     processor.save_pretrained(self.tmpdirname		)
     UpperCAmelCase :							Union[str, Any]								=				WavaVecaProcessor.from_pretrained(self.tmpdirname		)

     self.assertEqual(processor.tokenizer.get_vocab(),	tokenizer.get_vocab()		)
     self.assertIsInstance(processor.tokenizer,	__A		)

     self.assertEqual(processor.feature_extractor.to_json_string(),	feature_extractor.to_json_string()		)
     self.assertIsInstance(processor.feature_extractor,	__A		)



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							int								=				WavaVecaProcessor(tokenizer=self.get_tokenizer(),	feature_extractor=self.get_feature_extractor()		)
     processor.save_pretrained(self.tmpdirname		)

     UpperCAmelCase :							Dict								=				self.get_tokenizer(bos_token='''(BOS)''',	eos_token='''(EOS)'''		)
     UpperCAmelCase :							int								=				self.get_feature_extractor(do_normalize=__A,	padding_value=1.0		)

     UpperCAmelCase :							int								=				WavaVecaProcessor.from_pretrained(
         self.tmpdirname,	bos_token='''(BOS)''',	eos_token='''(EOS)''',	do_normalize=__A,	padding_value=1.0		)

     self.assertEqual(processor.tokenizer.get_vocab(),	tokenizer_add_kwargs.get_vocab()		)
     self.assertIsInstance(processor.tokenizer,	__A		)

     self.assertEqual(processor.feature_extractor.to_json_string(),	feature_extractor_add_kwargs.to_json_string()		)
     self.assertIsInstance(processor.feature_extractor,	__A		)



   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Optional[Any]								=				self.get_feature_extractor()
     UpperCAmelCase :							Optional[int]								=				self.get_tokenizer()

     UpperCAmelCase :							Any								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							Tuple								=				floats_list((3, 1_0_0_0)		)

     UpperCAmelCase :							Any								=				feature_extractor(__A,	return_tensors='''np'''		)
     UpperCAmelCase :							int								=				processor(__A,	return_tensors='''np'''		)

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum(),	input_processor[key].sum(),	delta=1E-2		)



   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Optional[Any]								=				self.get_feature_extractor()
     UpperCAmelCase :							Union[str, Any]								=				self.get_tokenizer()

     UpperCAmelCase :							List[Any]								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							List[str]								=				'''This is a test string'''

     UpperCAmelCase :							Tuple								=				processor(text=__A		)

     UpperCAmelCase :							Tuple								=				tokenizer(__A		)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key],	encoded_processor[key]		)



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							Tuple								=				self.get_feature_extractor()
     UpperCAmelCase :							int								=				self.get_tokenizer()

     UpperCAmelCase :							List[str]								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							Any								=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase :							str								=				processor.batch_decode(__A		)
     UpperCAmelCase :							List[str]								=				tokenizer.batch_decode(__A		)

     self.assertListEqual(__A,	__A		)





   def  __magic_name__					(							self							:  int		):
     UpperCAmelCase :							Optional[int]								=				self.get_feature_extractor()
     UpperCAmelCase :							List[str]								=				self.get_tokenizer()

     UpperCAmelCase :							Any								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     self.assertListEqual(
         processor.model_input_names,	feature_extractor.model_input_names,	msg='''`processor` and `feature_extractor` model input names do not match''',	)





