






"""simple docstring"""



import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
						from tokenizers import Tokenizer
						from tokenizers.models import WordPiece




class 							__lowercase(							unittest.TestCase	):






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=list(tokenizer_class.max_model_input_sizes.keys())
											for model_name in sa_models[:1]:
																lowerCamelCase__					=tokenizer_class.from_pretrained(_lowerCAmelCase)
																self.assertIsNotNone(_lowerCAmelCase)
																self.assertIsInstance(_lowerCAmelCase     ,    _lowerCAmelCase)
																self.assertIsInstance(_lowerCAmelCase     ,    _lowerCAmelCase)

																for special_tok in tokenizer.all_special_tokens:
																					self.assertIsInstance(_lowerCAmelCase     ,    _lowerCAmelCase)
																					lowerCamelCase__					=tokenizer.convert_tokens_to_ids(_lowerCAmelCase)
																					self.assertIsInstance(_lowerCAmelCase     ,    _lowerCAmelCase)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=pickle.dumps(_lowerCAmelCase)
											self.assertIsNotNone(_lowerCAmelCase)

											lowerCamelCase__					=pickle.loads(_lowerCAmelCase)

											# Ensure is_fast is correctly restored
											self.assertEqual(be_restored.is_fast     ,    be_original.is_fast)

											# Ensure encodings are potentially correctly restored
											if be_original.is_fast:
																self.assertIsNotNone(be_restored.encodings)
											else:
																self.assertIsNone(be_restored.encodings)

											# Ensure the keys are the same
											for original_v, restored_v in zip(be_original.values()     ,    be_restored.values()):
																if equal_op:
																					self.assertTrue(equal_op(_lowerCAmelCase     ,    _lowerCAmelCase))
																else:
																					self.assertEqual(_lowerCAmelCase     ,    _lowerCAmelCase)






						@slow
						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											self.check_tokenizer_from_pretrained(_lowerCAmelCase)






						def 		lowerCAmelCase (       self)      ->	Optional[int]:




											"""simple docstring"""





											self.assertEqual(TensorType('tf')     ,    TensorType.TENSORFLOW)
											self.assertEqual(TensorType('pt')     ,    TensorType.PYTORCH)
											self.assertEqual(TensorType('np')     ,    TensorType.NUMPY)






						@require_tokenizers
						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											import numpy as np

											lowerCamelCase__					=BertTokenizer.from_pretrained('bert-base-cased')
											lowerCamelCase__					=BertTokenizerFast.from_pretrained('bert-base-cased')

											# Python no tensor
											with self.subTest('BatchEncoding (Python, return_tensors=None)'):
																self.assert_dump_and_restore(tokenizer_p('Small example to encode'))

											with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'):
																self.assert_dump_and_restore(
																    tokenizer_p('Small example to encode'     ,    return_tensors=TensorType.NUMPY)     ,    np.array_equal)

											with self.subTest('BatchEncoding (Rust, return_tensors=None)'):
																self.assert_dump_and_restore(tokenizer_r('Small example to encode'))

											with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'):
																self.assert_dump_and_restore(
																    tokenizer_r('Small example to encode'     ,    return_tensors=TensorType.NUMPY)     ,    np.array_equal)






						@require_tf
						@require_tokenizers
						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											import tensorflow as tf

											def tf_array_equals(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																return tf.reduce_all(tf.equal(_lowerCAmelCase     ,    _lowerCAmelCase))

											lowerCamelCase__					=BertTokenizer.from_pretrained('bert-base-cased')
											lowerCamelCase__					=BertTokenizerFast.from_pretrained('bert-base-cased')

											with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'):
																self.assert_dump_and_restore(
																    tokenizer_p('Small example to encode'     ,    return_tensors=TensorType.TENSORFLOW)     ,    _lowerCAmelCase)

											with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'):
																self.assert_dump_and_restore(
																    tokenizer_r('Small example to encode'     ,    return_tensors=TensorType.TENSORFLOW)     ,    _lowerCAmelCase)






						@require_torch
						@require_tokenizers
						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											import torch

											lowerCamelCase__					=BertTokenizer.from_pretrained('bert-base-cased')
											lowerCamelCase__					=BertTokenizerFast.from_pretrained('bert-base-cased')

											with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'):
																self.assert_dump_and_restore(
																    tokenizer_p('Small example to encode'     ,    return_tensors=TensorType.PYTORCH)     ,    torch.equal)

											with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'):
																self.assert_dump_and_restore(
																    tokenizer_r('Small example to encode'     ,    return_tensors=TensorType.PYTORCH)     ,    torch.equal)






						@require_tokenizers
						def 		lowerCAmelCase (       self)      ->	int:




											"""simple docstring"""





											lowerCamelCase__					=BertTokenizer.from_pretrained('bert-base-cased')
											lowerCamelCase__					=BertTokenizerFast.from_pretrained('bert-base-cased')

											with self.subTest('Python Tokenizer'):
																self.assertFalse(tokenizer_p('Small example to_encode').is_fast)

											with self.subTest('Rust Tokenizer'):
																self.assertTrue(tokenizer_r('Small example to_encode').is_fast)






						@require_tokenizers
						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											lowerCamelCase__					=BertTokenizerFast.from_pretrained('bert-base-cased')
											lowerCamelCase__					=tokenizer_r(['Test', '\xad', 'test']     ,    is_split_into_words=_lowerCAmelCase)

											self.assertEqual(encoded.word_to_tokens(0)     ,    TokenSpan(start=1     ,    end=2))
											self.assertEqual(encoded.word_to_tokens(1)     ,    _lowerCAmelCase)
											self.assertEqual(encoded.word_to_tokens(2)     ,    TokenSpan(start=2     ,    end=3))






						def 		lowerCAmelCase (       self)      ->	int:




											"""simple docstring"""





											lowerCamelCase__					=BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]})
											lowerCamelCase__					=batch.convert_to_tensors(tensor_type='np')
											self.assertEqual(tensor_batch['inputs'].shape     ,    (2, 3))
											self.assertEqual(tensor_batch['labels'].shape     ,    (2,))
											# test converting the converted
											with CaptureStderr() as cs:
																lowerCamelCase__					=batch.convert_to_tensors(tensor_type='np')
											self.assertFalse(len(cs.err)     ,    msg=F"should have no warning, but got {cs.err}")

											lowerCamelCase__					=BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
											lowerCamelCase__					=batch.convert_to_tensors(tensor_type='np'     ,    prepend_batch_axis=_lowerCAmelCase)
											self.assertEqual(tensor_batch['inputs'].shape     ,    (1, 3))
											self.assertEqual(tensor_batch['labels'].shape     ,    (1,))






						@require_torch
						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											lowerCamelCase__					=BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]})
											lowerCamelCase__					=batch.convert_to_tensors(tensor_type='pt')
											self.assertEqual(tensor_batch['inputs'].shape     ,    (2, 3))
											self.assertEqual(tensor_batch['labels'].shape     ,    (2,))
											# test converting the converted
											with CaptureStderr() as cs:
																lowerCamelCase__					=batch.convert_to_tensors(tensor_type='pt')
											self.assertFalse(len(cs.err)     ,    msg=F"should have no warning, but got {cs.err}")

											lowerCamelCase__					=BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
											lowerCamelCase__					=batch.convert_to_tensors(tensor_type='pt'     ,    prepend_batch_axis=_lowerCAmelCase)
											self.assertEqual(tensor_batch['inputs'].shape     ,    (1, 3))
											self.assertEqual(tensor_batch['labels'].shape     ,    (1,))






						@require_tf
						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]})
											lowerCamelCase__					=batch.convert_to_tensors(tensor_type='tf')
											self.assertEqual(tensor_batch['inputs'].shape     ,    (2, 3))
											self.assertEqual(tensor_batch['labels'].shape     ,    (2,))
											# test converting the converted
											with CaptureStderr() as cs:
																lowerCamelCase__					=batch.convert_to_tensors(tensor_type='tf')
											self.assertFalse(len(cs.err)     ,    msg=F"should have no warning, but got {cs.err}")

											lowerCamelCase__					=BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
											lowerCamelCase__					=batch.convert_to_tensors(tensor_type='tf'     ,    prepend_batch_axis=_lowerCAmelCase)
											self.assertEqual(tensor_batch['inputs'].shape     ,    (1, 3))
											self.assertEqual(tensor_batch['labels'].shape     ,    (1,))






						@require_flax
						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											lowerCamelCase__					=BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]})
											lowerCamelCase__					=batch.convert_to_tensors(tensor_type='jax')
											self.assertEqual(tensor_batch['inputs'].shape     ,    (2, 3))
											self.assertEqual(tensor_batch['labels'].shape     ,    (2,))
											# test converting the converted
											with CaptureStderr() as cs:
																lowerCamelCase__					=batch.convert_to_tensors(tensor_type='jax')
											self.assertFalse(len(cs.err)     ,    msg=F"should have no warning, but got {cs.err}")

											lowerCamelCase__					=BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
											lowerCamelCase__					=batch.convert_to_tensors(tensor_type='jax'     ,    prepend_batch_axis=_lowerCAmelCase)
											self.assertEqual(tensor_batch['inputs'].shape     ,    (1, 3))
											self.assertEqual(tensor_batch['labels'].shape     ,    (1,))






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=[{"""input_ids""": np.array([0, 1, 2])}, {"""input_ids""": np.array([0, 1, 2, 3])}]
											lowerCamelCase__					=BertTokenizer.from_pretrained('bert-base-cased')

											lowerCamelCase__					=tokenizer.pad(_lowerCAmelCase     ,    padding=_lowerCAmelCase)
											self.assertTrue(isinstance(batch['input_ids']     ,    np.ndarray))
											self.assertEqual(batch['input_ids'].tolist()     ,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
											lowerCamelCase__					=tokenizer.pad(_lowerCAmelCase     ,    padding=_lowerCAmelCase     ,    return_tensors='np')
											self.assertTrue(isinstance(batch['input_ids']     ,    np.ndarray))
											self.assertEqual(batch['input_ids'].tolist()     ,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])






						@require_torch
						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											import torch

											lowerCamelCase__					=[{"""input_ids""": torch.tensor([0, 1, 2])}, {"""input_ids""": torch.tensor([0, 1, 2, 3])}]
											lowerCamelCase__					=BertTokenizer.from_pretrained('bert-base-cased')

											lowerCamelCase__					=tokenizer.pad(_lowerCAmelCase     ,    padding=_lowerCAmelCase)
											self.assertTrue(isinstance(batch['input_ids']     ,    torch.Tensor))
											self.assertEqual(batch['input_ids'].tolist()     ,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
											lowerCamelCase__					=tokenizer.pad(_lowerCAmelCase     ,    padding=_lowerCAmelCase     ,    return_tensors='pt')
											self.assertTrue(isinstance(batch['input_ids']     ,    torch.Tensor))
											self.assertEqual(batch['input_ids'].tolist()     ,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])






						@require_tf
						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											import tensorflow as tf

											lowerCamelCase__					=[{"""input_ids""": tf.constant([0, 1, 2])}, {"""input_ids""": tf.constant([0, 1, 2, 3])}]
											lowerCamelCase__					=BertTokenizer.from_pretrained('bert-base-cased')

											lowerCamelCase__					=tokenizer.pad(_lowerCAmelCase     ,    padding=_lowerCAmelCase)
											self.assertTrue(isinstance(batch['input_ids']     ,    tf.Tensor))
											self.assertEqual(batch['input_ids'].numpy().tolist()     ,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
											lowerCamelCase__					=tokenizer.pad(_lowerCAmelCase     ,    padding=_lowerCAmelCase     ,    return_tensors='tf')
											self.assertTrue(isinstance(batch['input_ids']     ,    tf.Tensor))
											self.assertEqual(batch['input_ids'].numpy().tolist()     ,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])






						@require_tokenizers
						def 		lowerCAmelCase (       self)      ->	int:




											"""simple docstring"""





											lowerCamelCase__					=Tokenizer(WordPiece(unk_token='[UNK]'))
											PreTrainedTokenizerFast(tokenizer_object=_lowerCAmelCase)






						@require_tokenizers
						def 		lowerCAmelCase (       self)      ->	Optional[int]:




											"""simple docstring"""





											lowerCamelCase__					=Tokenizer(WordPiece(unk_token='[UNK]'))
											with tempfile.TemporaryDirectory() as tmpdirname:
																bert_tokenizer.save(os.path.join(_lowerCAmelCase     ,    'tokenizer.json'))
																PreTrainedTokenizerFast(tokenizer_file=os.path.join(_lowerCAmelCase     ,    'tokenizer.json'))
import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	str          =	"config.json"

       @register_to_config
       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Tuple=2			,			_lowerCAmelCase					:     List[Any]=5			,			_lowerCAmelCase					:     int=(2, 5)			,			_lowerCAmelCase					:     Dict="for diffusion"			,			_lowerCAmelCase					:     List[str]=[1, 3]			,			):
           pass







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[int]          =	"config.json"

       @register_to_config
       def __init__(							self					:     int			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     Union[str, Any]=5			,			_lowerCAmelCase					:     Any=(2, 5)			,			_lowerCAmelCase					:     Optional[int]="for diffusion"			,			_lowerCAmelCase					:     int=[1, 3]			,			):
           pass







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	str          =	"config.json"

       @register_to_config
       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Optional[int]=2			,			_lowerCAmelCase					:     Optional[int]=5			,			_lowerCAmelCase					:     int=(2, 5)			,			_lowerCAmelCase					:     Tuple="for diffusion"			,			_lowerCAmelCase					:     Any=[1, 3]			,			_lowerCAmelCase					:     str=[1, 3]			,			):
           pass







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Union[str, Any]          =	"config.json"

       @register_to_config
       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     Optional[int]=5			,			_lowerCAmelCase					:     Optional[Any]=(2, 5)			,			_lowerCAmelCase					:     int="for diffusion"			,			_lowerCAmelCase					:     Dict=[1, 5]			,			_lowerCAmelCase					:     Optional[Any]=[5, 4]			,			):
           pass







class      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):

       def 			snake_case__    (							self					:     List[str]			):
           with self.assertRaises(_lowerCAmelCase			):
               ConfigMixin.load_config("""dummy_path"""			)

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				List[Any] 			=    SampleObject()
           __snake_case       :				Optional[Any] 			=    obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # init ignore private arguments
           __snake_case       :				Optional[Any] 			=    SampleObject(_name_or_path="""lalala"""			)
           __snake_case       :				List[Any] 			=    obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can override default
           __snake_case       :				int 			=    SampleObject(c=6			)
           __snake_case       :				Union[str, Any] 			=    obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can use positional arguments.
           __snake_case       :				List[str] 			=    SampleObject(1			,			c=6			)
           __snake_case       :				str 			=    obj.config
           assert config["a"] == 1
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case       :				Union[str, Any] 			=    SampleObject()
           __snake_case       :				Optional[Any] 			=    obj.config

           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           with tempfile.TemporaryDirectory() as tmpdirname:
               obj.save_config(_lowerCAmelCase			)
               __snake_case       :				List[Any] 			=    SampleObject.from_config(SampleObject.load_config(_lowerCAmelCase			)			)
               __snake_case       :				Union[str, Any] 			=    new_obj.config

           # unfreeze configs
           __snake_case       :				Optional[Any] 			=    dict(_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    dict(_lowerCAmelCase			)

           assert config.pop("""c"""			) == (2, 5)  # instantiated as tuple
           assert new_config.pop("""c"""			) == [2, 5]  # saved & loaded as list because of json
           config.pop("""_use_default_values"""			)
           assert config == new_config

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				Optional[Any] 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				List[Any] 			=    DDIMScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert ddim.__class__ == DDIMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 			snake_case__    (							self					:     Optional[int]			):
           __snake_case       :				int 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				Optional[int] 			=    EulerDiscreteScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert euler.__class__ == EulerDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				int 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				Any 			=    EulerAncestralDiscreteScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert euler.__class__ == EulerAncestralDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				str 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				Optional[int] 			=    PNDMScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert pndm.__class__ == PNDMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				int 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				List[str] 			=    DDPMScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			,			prediction_type="""sample"""			,			beta_end=8			,			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger_a:
               __snake_case       :				Optional[Any] 			=    DDPMScheduler.from_pretrained("""google/ddpm-celebahq-256"""			,			beta_start=88			)

           assert ddpm.__class__ == DDPMScheduler
           assert ddpm.config.prediction_type == "sample"
           assert ddpm.config.beta_end == 8
           assert ddpm_a.config.beta_start == 88

           # no warning should be thrown
           assert cap_logger.out == ""
           assert cap_logger_a.out == ""

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				List[Any] 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				str 			=    DPMSolverMultistepScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert dpm.__class__ == DPMSolverMultistepScheduler
           # no warning should be thrown
           assert cap_logger.out == ""





       def 			snake_case__    (							self					:     List[Any]			):
           # let's first save a config that should be in the form
           #    a=2,
           #    b=5,
           #    c=(2, 5),
           #    d="for diffusion",
           #    e=[1, 3],

           __snake_case       :				Optional[Any] 			=    SampleObject()

           __snake_case       :				Union[str, Any] 			=    {k: v for k, v in config.config.items() if not k.startswith("""_"""			)}

           # make sure that default config has all keys in `_use_default_values`
           assert set(config_dict.keys()			) == set(config.config._use_default_values			)

           with tempfile.TemporaryDirectory() as tmpdirname:
               config.save_config(_lowerCAmelCase			)

               # now loading it with SampleObject2 should put f into `_use_default_values`
               __snake_case       :				Union[str, Any] 			=    SampleObjecta.from_config(_lowerCAmelCase			)

               assert "f" in config._use_default_values
               assert config.f == [1, 3]

           # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
           # **BECAUSE** it is part of `config._use_default_values`
           __snake_case       :				Optional[int] 			=    SampleObjecta.from_config(config.config			)
           assert new_config.f == [5, 4]

           config.config._use_default_values.pop()
           __snake_case       :				Optional[Any] 			=    SampleObjecta.from_config(config.config			)
           assert new_config_a.f == [1, 3]

           # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
           assert new_config_a.e == [1, 3]



