
'''simple docstring'''







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 					lowerCAmelCase__  (    UpperCAmelCase_	):
					lowerCAmelCase_	  =	'config.json'






					@register_to_config
					def __init__(  self							,				__SCREAMING_SNAKE_CASE=2							,				__SCREAMING_SNAKE_CASE=5							,				__SCREAMING_SNAKE_CASE=(2, 5)							,				__SCREAMING_SNAKE_CASE="for diffusion"							,				__SCREAMING_SNAKE_CASE=[1, 3]							,				):


											"""simple docstring"""





											pass







class 					lowerCAmelCase__  (    UpperCAmelCase_	):
					lowerCAmelCase_	  =	'config.json'






					@register_to_config
					def __init__(  self							,				__SCREAMING_SNAKE_CASE=2							,				__SCREAMING_SNAKE_CASE=5							,				__SCREAMING_SNAKE_CASE=(2, 5)							,				__SCREAMING_SNAKE_CASE="for diffusion"							,				__SCREAMING_SNAKE_CASE=[1, 3]							,				):


											"""simple docstring"""





											pass







class 					lowerCAmelCase__  (    UpperCAmelCase_	):
					lowerCAmelCase_	  =	'config.json'






					@register_to_config
					def __init__(  self							,				__SCREAMING_SNAKE_CASE=2							,				__SCREAMING_SNAKE_CASE=5							,				__SCREAMING_SNAKE_CASE=(2, 5)							,				__SCREAMING_SNAKE_CASE="for diffusion"							,				__SCREAMING_SNAKE_CASE=[1, 3]							,				__SCREAMING_SNAKE_CASE=[1, 3]							,				):


											"""simple docstring"""





											pass







class 					lowerCAmelCase__  (    UpperCAmelCase_	):
					lowerCAmelCase_	  =	'config.json'






					@register_to_config
					def __init__(  self							,				__SCREAMING_SNAKE_CASE=2							,				__SCREAMING_SNAKE_CASE=5							,				__SCREAMING_SNAKE_CASE=(2, 5)							,				__SCREAMING_SNAKE_CASE="for diffusion"							,				__SCREAMING_SNAKE_CASE=[1, 5]							,				__SCREAMING_SNAKE_CASE=[5, 4]							,				):


											"""simple docstring"""





											pass







class 					lowerCAmelCase__  (    unittest.TestCase	):






					def       _snake_case       (  self						):


											"""simple docstring"""





											with self.assertRaises(_lowercase						):
																	ConfigMixin.load_config('''dummy_path'''						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			SampleObject()
											lowercase_      :	Dict						=			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
											lowercase_      :	str						=			SampleObject(_name_or_path='''lalala'''						)
											lowercase_      :	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
											lowercase_      :	Union[str, Any]						=			SampleObject(c=6						)
											lowercase_      :	int						=			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.
											lowercase_      :	Tuple						=			SampleObject(1							,				c=6						)
											lowercase_      :	Tuple						=			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						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			SampleObject()
											lowercase_      :	Optional[int]						=			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(_lowercase						)
																	lowercase_      :	List[Any]						=			SampleObject.from_config(SampleObject.load_config(_lowercase						)						)
																	lowercase_      :	Optional[Any]						=			new_obj.config

											# unfreeze configs
											lowercase_      :	Any						=			dict(_lowercase						)
											lowercase_      :	Optional[int]						=			dict(_lowercase						)

											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						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			logging.get_logger('''diffusers.configuration_utils'''						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(_lowercase						) as cap_logger:
																	lowercase_      :	Tuple						=			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						):


											"""simple docstring"""





											lowercase_      :	str						=			logging.get_logger('''diffusers.configuration_utils'''						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(_lowercase						) as cap_logger:
																	lowercase_      :	Tuple						=			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						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			logging.get_logger('''diffusers.configuration_utils'''						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(_lowercase						) as cap_logger:
																	lowercase_      :	Optional[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						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			logging.get_logger('''diffusers.configuration_utils'''						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(_lowercase						) as cap_logger:
																	lowercase_      :	str						=			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						):


											"""simple docstring"""





											lowercase_      :	Any						=			logging.get_logger('''diffusers.configuration_utils'''						)
											# 30 for warning
											logger.setLevel(30						)

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

											with CaptureLogger(_lowercase						) as cap_logger_a:
																	lowercase_      :	str						=			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						):


											"""simple docstring"""





											lowercase_      :	Any						=			logging.get_logger('''diffusers.configuration_utils'''						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(_lowercase						) as cap_logger:
																	lowercase_      :	List[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						):


											"""simple docstring"""





											lowercase_      :	int						=			SampleObject()

											lowercase_      :	int						=			{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(_lowercase						)

																	# now loading it with SampleObject2 should put f into `_use_default_values`
																	lowercase_      :	Any						=			SampleObjecta.from_config(_lowercase						)

																	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`
											lowercase_      :	Optional[int]						=			SampleObjecta.from_config(config.config						)
											assert new_config.f == [5, 4]

											config.config._use_default_values.pop()
											lowercase_      :	Tuple						=			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]











'''simple docstring'''

import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A:   Dict						=					get_tests_dir("fixtures/spiece.model")






@require_sentencepiece
@require_tokenizers
class 				__magic_name__  (						UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   int   =	ErnieMTokenizer
	SCREAMING_SNAKE_CASE_       :   Optional[Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	True
	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	False
	def 							lowerCamelCase__		(				self							)    ->	Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase_   : List[Any]   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)
							tokenizer.save_pretrained(self.tmpdirname							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : Optional[int]   	=      'this is a test'
							lowercase_   : Dict   	=      'this is a test'
							return input_text, output_text
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : str   	=      '<pad>'
							lowercase_   : Optional[Any]   	=      0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase							)	,   _lowercase							)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase							)	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : List[str]   	=      list(self.get_tokenizer().get_vocab().keys()							)

							self.assertEqual(vocab_keys[0]	,   '<pad>'							)
							self.assertEqual(vocab_keys[1]	,   '<unk>'							)
							self.assertEqual(vocab_keys[-1]	,   '▁eloquent'							)
							self.assertEqual(len(_lowercase							)	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							self.assertEqual(self.get_tokenizer().vocab_size	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							if not self.test_rust_tokenizer:
													return

							lowercase_   : Optional[int]   	=      self.get_tokenizer()
							lowercase_   : Optional[Any]   	=      self.get_rust_tokenizer()

							lowercase_   : Tuple   	=      'I was born in 92000, and this is falsé.'

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize(_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.tokenize(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Any   	=      tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Union[str, Any]   	=      self.get_rust_tokenizer()
							lowercase_   : Optional[Any]   	=      tokenizer.encode(_lowercase							)
							lowercase_   : str   	=      rust_tokenizer.encode(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Optional[int]   	=      ErnieMTokenizer(_lowercase	,   do_lower_case=_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : Tuple   	=      tokenizer.tokenize('This is a test'							)
							self.assertListEqual(_lowercase	,   ['▁this', '▁is', '▁a', '▁test']							)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase							)	,   [48, 25, 21, 1289]							)

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
							# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']							)
							lowercase_   : int   	=      tokenizer.convert_tokens_to_ids(_lowercase							)
							self.assertListEqual(_lowercase	,   [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]							)

							lowercase_   : Optional[int]   	=      tokenizer.convert_ids_to_tokens(_lowercase							)
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']	,   )
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : Tuple   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : List[Any]   	=      tokenizer.encode('sequence builders'							)
							lowercase_   : Optional[int]   	=      tokenizer.encode('multi-sequence build'							)

							lowercase_   : Union[str, Any]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase							)
							lowercase_   : Optional[int]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase	,   _lowercase							)

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
							    tokenizer.sep_token_id
							] + text_a + [tokenizer.sep_token_id]







	@slow
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							# fmt: off
							lowercase_   : Dict   	=      {'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_lowercase	,   model_name='susnato/ernie-m-base_pytorch'	,   sequences=[
							        'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
							        'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
							        'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
							        'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
							        'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
							        'conditioning on both left and right context in all layers.',
							        'The quick brown fox jumps over the lazy dog.',
							    ]	,   )
