





'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Tuple						    =   (DDIMParallelScheduler,)
       _snake_case     :					str						    =   (('eta', 0.0), ('num_inference_steps', 5_0))





       def snake_case__						(							self       :		Tuple	,			**lowerCAmelCase__       :		str					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	{
                '''num_train_timesteps''': 1000,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
                '''clip_sample''': True,
            }

            config.update(**lowerCAmelCase__					)
            return config





       def snake_case__						(							self       :		Tuple	,			**lowerCAmelCase__       :		Any					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config(**lowerCAmelCase__					)
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)

            _UpperCamelCase   ,    _UpperCamelCase       =	10, 0.0

            _UpperCamelCase       =	self.dummy_model()
            _UpperCamelCase       =	self.dummy_sample_deter

            scheduler.set_timesteps(lowerCAmelCase__					)

            for t in scheduler.timesteps:
                 _UpperCamelCase       =	model(lowerCAmelCase__	,			lowerCAmelCase__					)
                 _UpperCamelCase       =	scheduler.step(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					).prev_sample

            return sample





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Union[str, Any]:

            '''simple docstring'''




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

            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config(steps_offset=1					)
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)
            scheduler.set_timesteps(5					)
            assert torch.equal(scheduler.timesteps	,			torch.LongTensor([801, 601, 401, 201, 1]					)					)





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

            '''simple docstring'''




            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	,			[0.002, 0.02, 0.2, 2]					):
                 self.check_over_configs(beta_start=lowerCAmelCase__	,			beta_end=lowerCAmelCase__					)





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

            '''simple docstring'''




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





       def snake_case__						(							self       :		Union[str, Any]					)   ->					Dict:

            '''simple docstring'''




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





       def snake_case__						(							self       :		int					)   ->					List[Any]:

            '''simple docstring'''




            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=lowerCAmelCase__					)





       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=lowerCAmelCase__					)





       def snake_case__						(							self       :		List[str]					)   ->					Union[str, Any]:

            '''simple docstring'''




            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__					)





       def snake_case__						(							self       :		Any					)   ->					Union[str, Any]:

            '''simple docstring'''




            self.check_over_configs(thresholding=lowerCAmelCase__					)
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=lowerCAmelCase__	,			prediction_type=lowerCAmelCase__	,			sample_max_value=lowerCAmelCase__	,			)





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

            '''simple docstring'''




            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=lowerCAmelCase__					)





       def snake_case__						(							self       :		Tuple					)   ->					List[Any]:

            '''simple docstring'''




            for t, num_inference_steps in zip([1, 10, 50]	,			[10, 50, 500]					):
                 self.check_over_forward(time_step=lowerCAmelCase__	,			num_inference_steps=lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            for t, eta in zip([1, 10, 49]	,			[0.0, 0.5, 1.0]					):
                 self.check_over_forward(time_step=lowerCAmelCase__	,			eta=lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config()
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)

            assert torch.sum(torch.abs(scheduler._get_variance(0	,			0					) - 0.0					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(420	,			400					) - 0.14771					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(980	,			960					) - 0.32460					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(0	,			0					) - 0.0					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(487	,			486					) - 0.00979					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(999	,			998					) - 0.02					)					) < 1e-5





       def snake_case__						(							self       :		Tuple					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config()
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)

            _UpperCamelCase   ,    _UpperCamelCase       =	10, 0.0
            scheduler.set_timesteps(lowerCAmelCase__					)

            _UpperCamelCase       =	self.dummy_model()
            _UpperCamelCase       =	self.dummy_sample_deter
            _UpperCamelCase       =	self.dummy_sample_deter + 0.1
            _UpperCamelCase       =	self.dummy_sample_deter - 0.1

            _UpperCamelCase       =	samplea.shape[0]
            _UpperCamelCase       =	torch.stack([samplea, samplea, samplea]	,			dim=0					)
            _UpperCamelCase       =	torch.arange(lowerCAmelCase__					)[0:3, None].repeat(1	,			lowerCAmelCase__					)

            _UpperCamelCase       =	model(samples.flatten(0	,			1					)	,			timesteps.flatten(0	,			1					)					)
            _UpperCamelCase       =	scheduler.batch_step_no_noise(lowerCAmelCase__	,			timesteps.flatten(0	,			1					)	,			samples.flatten(0	,			1					)	,			lowerCAmelCase__					)

            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 1147.7904					) < 1e-2
            assert abs(result_mean.item() - 0.4982					) < 1e-3





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

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop()

            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 172.0067					) < 1e-2
            assert abs(result_mean.item() - 0.223967					) < 1e-3





       def snake_case__						(							self       :		Dict					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop(prediction_type='''v_prediction'''					)

            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 52.5302					) < 1e-2
            assert abs(result_mean.item() - 0.0684					) < 1e-3





       def snake_case__						(							self       :		List[str]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop(set_alpha_to_one=lowerCAmelCase__	,			beta_start=0.01					)
            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 149.8295					) < 1e-2
            assert abs(result_mean.item() - 0.1951					) < 1e-3







       def snake_case__						(							self       :		Dict					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop(set_alpha_to_one=lowerCAmelCase__	,			beta_start=0.01					)
            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 149.0784					) < 1e-2
            assert abs(result_mean.item() - 0.1941					) < 1e-3










'''simple docstring'''

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__						:      Optional[Any]	=    get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__						:      Any	=    get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

            # We have a SentencePiece fixture for testing
            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	'''<pad>'''
            _UpperCamelCase       =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__					)	,			lowerCAmelCase__					)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__					)	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,			'''<s>NOTUSED'''					)
            self.assertEqual(vocab_keys[1]	,			'''<pad>'''					)
            self.assertEqual(vocab_keys[-1]	,			'''<mask>'''					)
            self.assertEqual(len(lowerCAmelCase__					)	,			1004					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size	,			1005					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            # <unk> tokens are not the same for `rust` than for `slow`.
            # Because spm gives back raw token instead of `unk` in EncodeAsPieces
            # tokens = tokenizer.tokenize(sequence)
            _UpperCamelCase       =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''attention_mask''': [[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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]]}  # noqa: E501
            # fmt: on

            # camembert is a french model. So we also use french texts.
            _UpperCamelCase       =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=lowerCAmelCase__	,			model_name='''camembert-base'''	,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''	,			sequences=lowerCAmelCase__	,			)



