


import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


lowerCamelCase		   =			get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCamelCase		   =			get_tests_dir('fixtures/vocab.json')
lowerCamelCase		   =			get_tests_dir('fixtures')




class A    (  unittest.TestCase	):
      UpperCamelCase__				:    Dict =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =0






      def 			lowerCamelCase			(   self		:	str					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'					)
             self.assertIsInstance(lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Tuple:
             """simple docstring"""




             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase			:						int            =WavaVecaConfig()
                    _lowerCamelCase			:						Dict            =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'					)

                    # save in new folder
                    model_config.save_pretrained(lowercase_					)
                    processor.save_pretrained(lowercase_					)

                    _lowerCamelCase			:						Any            =AutoProcessor.from_pretrained(lowercase_					)

             self.assertIsInstance(lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	str					)   ->		Union[str, Any]:
             """simple docstring"""




             with tempfile.TemporaryDirectory() as tmpdirname:
                    # copy relevant files
                    copyfile(lowercase_     ,  os.path.join(lowercase_     ,  lowercase_					)					)
                    copyfile(lowercase_     ,  os.path.join(lowercase_     ,  'vocab.json'					)					)

                    _lowerCamelCase			:						Union[str, Any]            =AutoProcessor.from_pretrained(lowercase_					)

             self.assertIsInstance(lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Any					)   ->		Optional[Any]:
             """simple docstring"""




             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase			:						List[Any]            =WavaVecaFeatureExtractor()
                    _lowerCamelCase			:						List[str]            =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'					)

                    _lowerCamelCase			:						str            =WavaVecaProcessor(lowercase_     ,  lowercase_					)

                    # save in new folder
                    processor.save_pretrained(lowercase_					)

                    # drop `processor_class` in tokenizer
                    with open(os.path.join(lowercase_     ,  lowercase_					)     ,  'r'					) as f:
                           _lowerCamelCase			:						Optional[int]            =json.load(lowercase_					)
                           config_dict.pop('processor_class'					)

                    with open(os.path.join(lowercase_     ,  lowercase_					)     ,  'w'					) as f:
                           f.write(json.dumps(lowercase_					)					)

                    _lowerCamelCase			:						Optional[int]            =AutoProcessor.from_pretrained(lowercase_					)

             self.assertIsInstance(lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Optional[int]:
             """simple docstring"""




             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase			:						Optional[Any]            =WavaVecaFeatureExtractor()
                    _lowerCamelCase			:						Tuple            =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'					)

                    _lowerCamelCase			:						Dict            =WavaVecaProcessor(lowercase_     ,  lowercase_					)

                    # save in new folder
                    processor.save_pretrained(lowercase_					)

                    # drop `processor_class` in feature extractor
                    with open(os.path.join(lowercase_     ,  lowercase_					)     ,  'r'					) as f:
                           _lowerCamelCase			:						Union[str, Any]            =json.load(lowercase_					)
                           config_dict.pop('processor_class'					)

                    with open(os.path.join(lowercase_     ,  lowercase_					)     ,  'w'					) as f:
                           f.write(json.dumps(lowercase_					)					)

                    _lowerCamelCase			:						Optional[int]            =AutoProcessor.from_pretrained(lowercase_					)

             self.assertIsInstance(lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase			:						Optional[Any]            =WavaVecaConfig(processor_class='Wav2Vec2Processor'					)
                    model_config.save_pretrained(lowercase_					)
                    # copy relevant files
                    copyfile(lowercase_     ,  os.path.join(lowercase_     ,  'vocab.json'					)					)
                    # create emtpy sample processor
                    with open(os.path.join(lowercase_     ,  lowercase_					)     ,  'w'					) as f:
                           f.write('{}'					)

                    _lowerCamelCase			:						int            =AutoProcessor.from_pretrained(lowercase_					)

             self.assertIsInstance(lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[int]:
             """simple docstring"""




             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						int            =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'					)
             # If remote code is disabled, we can't load this config.
             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						Union[str, Any]            =AutoProcessor.from_pretrained(
                        'hf-internal-testing/test_dynamic_processor'     ,  trust_remote_code=lowercase_					)

             _lowerCamelCase			:						List[str]            =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'     ,  trust_remote_code=lowercase_					)
             self.assertTrue(processor.special_attribute_present					)
             self.assertEqual(processor.__class__.__name__     ,  'NewProcessor'					)

             _lowerCamelCase			:						int            =processor.feature_extractor
             self.assertTrue(feature_extractor.special_attribute_present					)
             self.assertEqual(feature_extractor.__class__.__name__     ,  'NewFeatureExtractor'					)

             _lowerCamelCase			:						Optional[int]            =processor.tokenizer
             self.assertTrue(tokenizer.special_attribute_present					)
             if is_tokenizers_available():
                    self.assertEqual(tokenizer.__class__.__name__     ,  'NewTokenizerFast'					)

                    # Test we can also load the slow version
                    _lowerCamelCase			:						int            =AutoProcessor.from_pretrained(
                        'hf-internal-testing/test_dynamic_processor'     ,  trust_remote_code=lowercase_     ,  use_fast=lowercase_					)
                    _lowerCamelCase			:						Optional[int]            =new_processor.tokenizer
                    self.assertTrue(new_tokenizer.special_attribute_present					)
                    self.assertEqual(new_tokenizer.__class__.__name__     ,  'NewTokenizer'					)
             else:
                    self.assertEqual(tokenizer.__class__.__name__     ,  'NewTokenizer'					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




             try:
                    AutoConfig.register('custom'     ,  lowercase_					)
                    AutoFeatureExtractor.register(lowercase_     ,  lowercase_					)
                    AutoTokenizer.register(lowercase_     ,  slow_tokenizer_class=lowercase_					)
                    AutoProcessor.register(lowercase_     ,  lowercase_					)
                    # Trying to register something existing in the Transformers library will raise an error
                    with self.assertRaises(lowercase_					):
                           AutoProcessor.register(lowercase_     ,  lowercase_					)

                    # Now that the config is registered, it can be used as any other config with the auto-API
                    _lowerCamelCase			:						str            =CustomFeatureExtractor.from_pretrained(lowercase_					)

                    with tempfile.TemporaryDirectory() as tmp_dir:
                           _lowerCamelCase			:						str            =os.path.join(lowercase_     ,  'vocab.txt'					)
                           with open(lowercase_     ,  'w'     ,  encoding='utf-8'					) as vocab_writer:
                                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]					)					)
                           _lowerCamelCase			:						List[Any]            =CustomTokenizer(lowercase_					)

                    _lowerCamelCase			:						Optional[int]            =CustomProcessor(lowercase_     ,  lowercase_					)

                    with tempfile.TemporaryDirectory() as tmp_dir:
                           processor.save_pretrained(lowercase_					)
                           _lowerCamelCase			:						List[Any]            =AutoProcessor.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]
                    if CustomConfig in TOKENIZER_MAPPING._extra_content:
                           del TOKENIZER_MAPPING._extra_content[CustomConfig]
                    if CustomConfig in PROCESSOR_MAPPING._extra_content:
                           del PROCESSOR_MAPPING._extra_content[CustomConfig]






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		List[str]:
             """simple docstring"""




             class A    (  UpperCamelCase_	):
                   UpperCamelCase__				:    Optional[Any] =False




             class A    (  UpperCamelCase_	):
                   UpperCamelCase__				:    int =False




             class A    (  UpperCamelCase_	):
                   UpperCamelCase__				:    Union[str, Any] ='AutoFeatureExtractor'
                   UpperCamelCase__				:    str ='AutoTokenizer'
                   UpperCamelCase__				:    List[Any] =False

             try:
                    AutoConfig.register('custom'     ,  lowercase_					)
                    AutoFeatureExtractor.register(lowercase_     ,  lowercase_					)
                    AutoTokenizer.register(lowercase_     ,  slow_tokenizer_class=lowercase_					)
                    AutoProcessor.register(lowercase_     ,  lowercase_					)
                    # If remote code is not set, the default is to use local classes.
                    _lowerCamelCase			:						int            =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'					)
                    self.assertEqual(processor.__class__.__name__     ,  'NewProcessor'					)
                    self.assertFalse(processor.special_attribute_present					)
                    self.assertFalse(processor.feature_extractor.special_attribute_present					)
                    self.assertFalse(processor.tokenizer.special_attribute_present					)

                    # If remote code is disabled, we load the local ones.
                    _lowerCamelCase			:						int            =AutoProcessor.from_pretrained(
                        'hf-internal-testing/test_dynamic_processor'     ,  trust_remote_code=lowercase_					)
                    self.assertEqual(processor.__class__.__name__     ,  'NewProcessor'					)
                    self.assertFalse(processor.special_attribute_present					)
                    self.assertFalse(processor.feature_extractor.special_attribute_present					)
                    self.assertFalse(processor.tokenizer.special_attribute_present					)

                    # If remote is enabled, we load from the Hub.
                    _lowerCamelCase			:						str            =AutoProcessor.from_pretrained(
                        'hf-internal-testing/test_dynamic_processor'     ,  trust_remote_code=lowercase_					)
                    self.assertEqual(processor.__class__.__name__     ,  'NewProcessor'					)
                    self.assertTrue(processor.special_attribute_present					)
                    self.assertTrue(processor.feature_extractor.special_attribute_present					)
                    self.assertTrue(processor.tokenizer.special_attribute_present					)

             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]
                    if CustomConfig in TOKENIZER_MAPPING._extra_content:
                           del TOKENIZER_MAPPING._extra_content[CustomConfig]
                    if CustomConfig in PROCESSOR_MAPPING._extra_content:
                           del PROCESSOR_MAPPING._extra_content[CustomConfig]






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert'					)
             self.assertEqual(processor.__class__.__name__     ,  'BertTokenizerFast'					)






      def 			lowerCamelCase			(   self		:	Any					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Any            =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext'					)
             self.assertEqual(processor.__class__.__name__     ,  'ConvNextImageProcessor'					)




@is_staging_test
class A    (  unittest.TestCase	):
      UpperCamelCase__				:    List[Any] =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']






      @classmethod
      def 			lowerCamelCase			(   cls		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =TOKEN
             HfFolder.save_token(lowercase_					)






      @classmethod
      def 			lowerCamelCase			(   cls		:	Optional[int]					)   ->		Union[str, Any]:
             """simple docstring"""




             try:
                    delete_repo(token=cls._token     ,  repo_id='test-processor'					)
             except HTTPError:
                    pass

             try:
                    delete_repo(token=cls._token     ,  repo_id='valid_org/test-processor-org'					)
             except HTTPError:
                    pass

             try:
                    delete_repo(token=cls._token     ,  repo_id='test-dynamic-processor'					)
             except HTTPError:
                    pass






      def 			lowerCamelCase			(   self		:	str					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =WavaVecaProcessor.from_pretrained(lowercase_					)
             with tempfile.TemporaryDirectory() as tmp_dir:
                    processor.save_pretrained(
                        os.path.join(lowercase_     ,  'test-processor'					)     ,  push_to_hub=lowercase_     ,  use_auth_token=self._token					)

                    _lowerCamelCase			:						Union[str, Any]            =WavaVecaProcessor.from_pretrained(F'''{USER}/test-processor'''					)
                    for k, v in processor.feature_extractor.__dict__.items():
                           self.assertEqual(lowercase_     ,  getattr(new_processor.feature_extractor     ,  lowercase_					)					)
                    self.assertDictEqual(new_processor.tokenizer.get_vocab()     ,  processor.tokenizer.get_vocab()					)






      def 			lowerCamelCase			(   self		:	str					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						int            =WavaVecaProcessor.from_pretrained(lowercase_					)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    processor.save_pretrained(
                        os.path.join(lowercase_     ,  'test-processor-org'					)     ,  push_to_hub=lowercase_     ,  use_auth_token=self._token     ,  organization='valid_org'     ,  )

                    _lowerCamelCase			:						str            =WavaVecaProcessor.from_pretrained('valid_org/test-processor-org'					)
                    for k, v in processor.feature_extractor.__dict__.items():
                           self.assertEqual(lowercase_     ,  getattr(new_processor.feature_extractor     ,  lowercase_					)					)
                    self.assertDictEqual(new_processor.tokenizer.get_vocab()     ,  processor.tokenizer.get_vocab()					)






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		str:
             """simple docstring"""




             CustomFeatureExtractor.register_for_auto_class()
             CustomTokenizer.register_for_auto_class()
             CustomProcessor.register_for_auto_class()

             _lowerCamelCase			:						Optional[Any]            =CustomFeatureExtractor.from_pretrained(lowercase_					)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    _lowerCamelCase			:						Dict            =os.path.join(lowercase_     ,  'vocab.txt'					)
                    with open(lowercase_     ,  'w'     ,  encoding='utf-8'					) as vocab_writer:
                           vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]					)					)
                    _lowerCamelCase			:						Any            =CustomTokenizer(lowercase_					)

             _lowerCamelCase			:						List[Any]            =CustomProcessor(lowercase_     ,  lowercase_					)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    create_repo(F'''{USER}/test-dynamic-processor'''     ,  token=self._token					)
                    _lowerCamelCase			:						List[str]            =Repository(lowercase_     ,  clone_from=F'''{USER}/test-dynamic-processor'''     ,  token=self._token					)
                    processor.save_pretrained(lowercase_					)

                    # This has added the proper auto_map field to the feature extractor config
                    self.assertDictEqual(
                        processor.feature_extractor.auto_map     ,  {
                            'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
                            'AutoProcessor': 'custom_processing.CustomProcessor',
                        }     ,  )

                    # This has added the proper auto_map field to the tokenizer config
                    with open(os.path.join(lowercase_     ,  'tokenizer_config.json'					)					) as f:
                           _lowerCamelCase			:						Union[str, Any]            =json.load(lowercase_					)
                    self.assertDictEqual(
                        tokenizer_config['auto_map']     ,  {
                            'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
                            'AutoProcessor': 'custom_processing.CustomProcessor',
                        }     ,  )

                    # The code has been copied from fixtures
                    self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  'custom_feature_extraction.py'					)					)					)
                    self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  'custom_tokenization.py'					)					)					)
                    self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  'custom_processing.py'					)					)					)

                    repo.push_to_hub()

             _lowerCamelCase			:						Tuple            =AutoProcessor.from_pretrained(F'''{USER}/test-dynamic-processor'''     ,  trust_remote_code=lowercase_					)
             # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
             self.assertEqual(new_processor.__class__.__name__     ,  'CustomProcessor'					)






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =(PNDMScheduler,)
      UpperCamelCase__				:    Dict =(('num_inference_steps', 50),)






      def 			lowerCamelCase			(   self		:	Dict     ,  **lowercase_		:	Dict					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            ={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
             }

             config.update(**lowercase_					)
             return config






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	str=0     ,  **lowercase_		:	str					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =dict(self.forward_default_kwargs					)
             _lowerCamelCase			:						Optional[Any]            =kwargs.pop('num_inference_steps'     ,  lowercase_					)
             _lowerCamelCase			:						Tuple            =self.dummy_sample
             _lowerCamelCase			:						int            =0.1 * sample
             _lowerCamelCase			:						int            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						str            =self.get_scheduler_config(**lowercase_					)
                    _lowerCamelCase			:						str            =scheduler_class(**lowercase_					)
                    scheduler.set_timesteps(lowercase_					)
                    # copy over dummy past residuals
                    _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =scheduler_class.from_pretrained(lowercase_					)
                           new_scheduler.set_timesteps(lowercase_					)
                           # copy over dummy past residuals
                           _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    _lowerCamelCase			:						Union[str, Any]            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						List[str]            =new_scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Optional[int]            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						Optional[Any]            =new_scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	List[str]=0     ,  **lowercase_		:	int					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Any            =dict(self.forward_default_kwargs					)
             _lowerCamelCase			:						Dict            =kwargs.pop('num_inference_steps'     ,  lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample
             _lowerCamelCase			:						Optional[int]            =0.1 * sample
             _lowerCamelCase			:						List[str]            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						Any            =self.get_scheduler_config()
                    _lowerCamelCase			:						int            =scheduler_class(**lowercase_					)
                    scheduler.set_timesteps(lowercase_					)

                    # copy over dummy past residuals (must be after setting timesteps)
                    _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =scheduler_class.from_pretrained(lowercase_					)
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(lowercase_					)

                           # copy over dummy past residual (must be after setting timesteps)
                           _lowerCamelCase			:						Optional[int]            =dummy_past_residuals[:]

                    _lowerCamelCase			:						List[str]            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						str            =new_scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Optional[Any]            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						List[str]            =new_scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  **lowercase_		:	Any					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.scheduler_classes[0]
             _lowerCamelCase			:						Optional[Any]            =self.get_scheduler_config(**lowercase_					)
             _lowerCamelCase			:						Any            =scheduler_class(**lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            =10
             _lowerCamelCase			:						str            =self.dummy_model()
             _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_					)

             for i, t in enumerate(scheduler.prk_timesteps					):
                    _lowerCamelCase			:						Union[str, Any]            =model(lowercase_     ,  lowercase_					)
                    _lowerCamelCase			:						Any            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample

             for i, t in enumerate(scheduler.plms_timesteps					):
                    _lowerCamelCase			:						List[Any]            =model(lowercase_     ,  lowercase_					)
                    _lowerCamelCase			:						int            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample

             return sample






      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =dict(self.forward_default_kwargs					)

             _lowerCamelCase			:						Optional[int]            =kwargs.pop('num_inference_steps'     ,  lowercase_					)

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						List[str]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)

                    _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample
                    _lowerCamelCase			:						Optional[int]            =0.1 * sample

                    if num_inference_steps is not None and hasattr(lowercase_     ,  'set_timesteps'					):
                           scheduler.set_timesteps(lowercase_					)
                    elif num_inference_steps is not None and not hasattr(lowercase_     ,  'set_timesteps'					):
                           _lowerCamelCase			:						Tuple            =num_inference_steps

                    # copy over dummy past residuals (must be done after set_timesteps)
                    _lowerCamelCase			:						List[Any]            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                    _lowerCamelCase			:						Union[str, Any]            =dummy_past_residuals[:]

                    _lowerCamelCase			:						List[Any]            =scheduler.step_prk(lowercase_     ,  0     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						str            =scheduler.step_prk(lowercase_     ,  1     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Tuple            =scheduler.step_plms(lowercase_     ,  0     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						Optional[Any]            =scheduler.step_plms(lowercase_     ,  1     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Any:
             """simple docstring"""




             for timesteps in [100, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=lowercase_					)

             _lowerCamelCase			:						Optional[int]            =self.scheduler_classes[0]
             _lowerCamelCase			:						Dict            =self.get_scheduler_config(steps_offset=1					)
             _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)
             scheduler.set_timesteps(10					)
             assert torch.equal(
                 scheduler.timesteps     ,  torch.LongTensor(
                     [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)     ,  )






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		str:
             """simple docstring"""




             for beta_start, beta_end in zip([0.0001, 0.001]     ,  [0.002, 0.02]					):
                    self.check_over_configs(beta_start=lowercase_     ,  beta_end=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Union[str, Any]:
             """simple docstring"""




             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Any:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             for t in [1, 5, 10]:
                    self.check_over_forward(time_step=lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[Any]:
             """simple docstring"""




             for t, num_inference_steps in zip([1, 5, 10]     ,  [10, 50, 100]					):
                    self.check_over_forward(num_inference_steps=lowercase_					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =27

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						Dict            =self.dummy_sample
                    _lowerCamelCase			:						List[Any]            =0.1 * sample

                    _lowerCamelCase			:						List[Any]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[str]            =scheduler_class(**lowercase_					)

                    scheduler.set_timesteps(lowercase_					)

                    # before power of 3 fix, would error on first step, so we only need to do two
                    for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                           _lowerCamelCase			:						Tuple            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Optional[int]:
             """simple docstring"""




             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						Dict            =self.scheduler_classes[0]
                    _lowerCamelCase			:						Optional[Any]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)

                    scheduler.step_plms(self.dummy_sample     ,  1     ,  self.dummy_sample					).prev_sample






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.full_loop()
             _lowerCamelCase			:						Optional[Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						Union[str, Any]            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 198.1318					) < 1E-2
             assert abs(result_mean.item() - 0.2580					) < 1E-3






      def 			lowerCamelCase			(   self		:	Any					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.full_loop(prediction_type='v_prediction'					)
             _lowerCamelCase			:						Dict            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						str            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 67.3986					) < 1E-2
             assert abs(result_mean.item() - 0.0878					) < 1E-3






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.full_loop(set_alpha_to_one=lowercase_     ,  beta_start=0.01					)
             _lowerCamelCase			:						Optional[Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						Union[str, Any]            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 230.0399					) < 1E-2
             assert abs(result_mean.item() - 0.2995					) < 1E-3






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.full_loop(set_alpha_to_one=lowercase_     ,  beta_start=0.01					)
             _lowerCamelCase			:						Union[str, Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						str            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 186.9482					) < 1E-2
             assert abs(result_mean.item() - 0.2434					) < 1E-3


