

'''simple docstring'''

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     (DDPMScheduler,)



				def   a__					(    self,     **_lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				{
											    'num_train_timesteps': 1000,
											    'beta_start': 0.0_001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											    'variance_type': 'fixed_small',
											    'clip_sample': True,
											}

											config.update(**_lowercase       )
											return config



				def   a__					(    self       )     ->							List[str]:
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=_lowercase       )



				def   a__					(    self       )     ->							str:
											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1],     [0.002, 0.02, 0.2, 2]       ):
																		self.check_over_configs(beta_start=_lowercase,     beta_end=_lowercase       )



				def   a__					(    self       )     ->							List[Any]:
											for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=_lowercase       )



				def   a__					(    self       )     ->							Optional[Any]:
											for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=_lowercase       )



				def   a__					(    self       )     ->							List[str]:
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_lowercase       )



				def   a__					(    self       )     ->							int:
											self.check_over_configs(thresholding=_lowercase       )
											for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=_lowercase,     prediction_type=_lowercase,     sample_max_value=_lowercase,     )



				def   a__					(    self       )     ->							Optional[Any]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(prediction_type=_lowercase       )



				def   a__					(    self       )     ->							Optional[int]:
											for t in [0, 500, 999]:
																		self.check_over_forward(time_step=_lowercase       )



				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_				=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_				=				scheduler_class(**_lowercase       )

											assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.00_979       )       ) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1E-5



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_				=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_				=				scheduler_class(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )

											SCREAMING_SNAKE_CASE_				=				self.dummy_model()
											SCREAMING_SNAKE_CASE_				=				self.dummy_sample_deter
											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )

											for t in reversed(range(_lowercase       )       ):
																		# 1. predict noise residual
																		SCREAMING_SNAKE_CASE_				=				model(_lowercase,     _lowercase       )

																		# 2. predict previous mean of sample x_t-1
																		SCREAMING_SNAKE_CASE_				=				scheduler.step(_lowercase,     _lowercase,     _lowercase,     generator=_lowercase       ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		SCREAMING_SNAKE_CASE_				=				pred_prev_sample

											SCREAMING_SNAKE_CASE_				=				torch.sum(torch.abs(_lowercase       )       )
											SCREAMING_SNAKE_CASE_				=				torch.mean(torch.abs(_lowercase       )       )

											assert abs(result_sum.item() - 258.9_606       ) < 1E-2
											assert abs(result_mean.item() - 0.3_372       ) < 1E-3



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_				=				self.get_scheduler_config(prediction_type='v_prediction'       )
											SCREAMING_SNAKE_CASE_				=				scheduler_class(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )

											SCREAMING_SNAKE_CASE_				=				self.dummy_model()
											SCREAMING_SNAKE_CASE_				=				self.dummy_sample_deter
											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )

											for t in reversed(range(_lowercase       )       ):
																		# 1. predict noise residual
																		SCREAMING_SNAKE_CASE_				=				model(_lowercase,     _lowercase       )

																		# 2. predict previous mean of sample x_t-1
																		SCREAMING_SNAKE_CASE_				=				scheduler.step(_lowercase,     _lowercase,     _lowercase,     generator=_lowercase       ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		SCREAMING_SNAKE_CASE_				=				pred_prev_sample

											SCREAMING_SNAKE_CASE_				=				torch.sum(torch.abs(_lowercase       )       )
											SCREAMING_SNAKE_CASE_				=				torch.mean(torch.abs(_lowercase       )       )

											assert abs(result_sum.item() - 202.0_296       ) < 1E-2
											assert abs(result_mean.item() - 0.2_631       ) < 1E-3



				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_				=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_				=				scheduler_class(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				[100, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				scheduler.timesteps

											for i, timestep in enumerate(_lowercase       ):
																		if i == len(_lowercase       ) - 1:
																									SCREAMING_SNAKE_CASE_				=				-1
																		else:
																									SCREAMING_SNAKE_CASE_				=				timesteps[i + 1]

																		SCREAMING_SNAKE_CASE_				=				scheduler.previous_timestep(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				prev_t.item()

																		self.assertEqual(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_				=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_				=				scheduler_class(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				[100, 87, 50, 51, 0]

											with self.assertRaises(_lowercase,     msg='`custom_timesteps` must be in descending order.'       ):
																		scheduler.set_timesteps(timesteps=_lowercase       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_				=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_				=				scheduler_class(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				[100, 87, 50, 1, 0]
											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )

											with self.assertRaises(_lowercase,     msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'       ):
																		scheduler.set_timesteps(num_inference_steps=_lowercase,     timesteps=_lowercase       )





				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_				=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_				=				scheduler_class(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    _lowercase,     msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}',     ):
																		scheduler.set_timesteps(timesteps=_lowercase       )








'''simple docstring'''

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


SCREAMING_SNAKE_CASE							:				int     	=			get_tests_dir("fixtures")
SCREAMING_SNAKE_CASE							:				str     	=			get_tests_dir("fixtures/dummy_feature_extractor_config.json")
SCREAMING_SNAKE_CASE							:				str     	=			get_tests_dir("fixtures/dummy-config.json")






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				0



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							str:
											with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE_				=				WavaVecaConfig()

																		# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       ).to_dict()

																		config_dict.pop('feature_extractor_type'       )
																		SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor(**_lowercase       )

																		# save in new folder
																		model_config.save_pretrained(_lowercase       )
																		config.save_pretrained(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )

																		# make sure private variable is not incorrectly saved
																		SCREAMING_SNAKE_CASE_				=				json.loads(config.to_json_string()       )
																		self.assertTrue('_processor_class' not in dict_as_saved       )

											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Any:
											with self.assertRaisesRegex(
											    _lowercase,     'bert-base is not a local folder and is not a valid model identifier'       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('bert-base'       )



				def   a__					(    self       )     ->							List[Any]:
											with self.assertRaisesRegex(
											    _lowercase,     R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase,     revision='aaaaaa'       )



				def   a__					(    self       )     ->							List[Any]:
											with self.assertRaisesRegex(
											    _lowercase,     'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.',     ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'       )



				def   a__					(    self       )     ->							List[str]:
											# If remote code is not set, we will time out when asking whether to load the model.
											with self.assertRaises(_lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       )
											# If remote code is disabled, we can't load this config.
											with self.assertRaises(_lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
											    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
											self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )

											# Test feature extractor can be reloaded.
											with tempfile.TemporaryDirectory() as tmp_dir:
																		feature_extractor.save_pretrained(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase,     trust_remote_code=_lowercase       )
											self.assertEqual(reloaded_feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )



				def   a__					(    self       )     ->							int:
											try:
																		AutoConfig.register('custom',     _lowercase       )
																		AutoFeatureExtractor.register(_lowercase,     _lowercase       )
																		# Trying to register something existing in the Transformers library will raise an error
																		with self.assertRaises(_lowercase       ):
																									AutoFeatureExtractor.register(_lowercase,     _lowercase       )

																		# Now that the config is registered, it can be used as any other config with the auto-API
																		SCREAMING_SNAKE_CASE_				=				CustomFeatureExtractor.from_pretrained(_lowercase       )
																		with tempfile.TemporaryDirectory() as tmp_dir:
																									feature_extractor.save_pretrained(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
																									self.assertIsInstance(_lowercase,     _lowercase       )

											finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





				def   a__					(    self       )     ->							str:






											class    snake_case     (      lowercase_		):






															"""simple docstring"""
															_a    	=     True

											try:
																		AutoConfig.register('custom',     _lowercase       )
																		AutoFeatureExtractor.register(_lowercase,     _lowercase       )
																		# If remote code is not set, the default is to use local
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(feature_extractor.is_local       )

																		# If remote code is disabled, we load the local one.
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(feature_extractor.is_local       )

																		# If remote is enabled, we load from the Hub
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(not hasattr(_lowercase,     'is_local'       )       )

											finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





