
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class        _UpperCamelCase				(					A_  ):






			'''simple docstring'''

			__UpperCamelCase      :						Union[str, Any]					      = (DEISMultistepScheduler,)
			__UpperCamelCase      :						Any					      = (('''num_inference_steps''', 25),)

			def       lowerCAmelCase__	(       self :       List[Any]						,	**snake_case_ :       List[str]      ):
							UpperCamelCase_:							int	=						{
							    """num_train_timesteps""": 1000,
							    """beta_start""": 0.0001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """solver_order""": 2,
							}

							config.update(**snake_case_      )
							return config

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       Optional[int]=0						,	**snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							Dict	=						dict(self.forward_default_kwargs      )
							UpperCamelCase_:							Optional[int]	=						kwargs.pop("""num_inference_steps"""						,	snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						self.dummy_sample
							UpperCamelCase_:							List[Any]	=						0.1 * sample
							UpperCamelCase_:							Optional[Any]	=						[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
											UpperCamelCase_:							List[Any]	=						self.get_scheduler_config(**snake_case_      )
											UpperCamelCase_:							List[str]	=						scheduler_class(**snake_case_      )
											scheduler.set_timesteps(snake_case_      )
											# copy over dummy past residuals
											UpperCamelCase_:							Union[str, Any]	=						dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(snake_case_      )
															UpperCamelCase_:							List[str]	=						scheduler_class.from_pretrained(snake_case_      )
															new_scheduler.set_timesteps(snake_case_      )
															# copy over dummy past residuals
															UpperCamelCase_:							Dict	=						dummy_past_residuals[: new_scheduler.config.solver_order]

											UpperCamelCase_:							Dict	=						sample, sample
											for t in range(snake_case_						,	time_step + scheduler.config.solver_order + 1      ):
															UpperCamelCase_:							Optional[int]	=						scheduler.step(snake_case_						,	snake_case_						,	snake_case_						,	**snake_case_      ).prev_sample
															UpperCamelCase_:							str	=						new_scheduler.step(snake_case_						,	snake_case_						,	snake_case_						,	**snake_case_      ).prev_sample

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

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							pass

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       int=0						,	**snake_case_ :       Tuple      ):
							UpperCamelCase_:							int	=						dict(self.forward_default_kwargs      )
							UpperCamelCase_:							Any	=						kwargs.pop("""num_inference_steps"""						,	snake_case_      )
							UpperCamelCase_:							Tuple	=						self.dummy_sample
							UpperCamelCase_:							Optional[Any]	=						0.1 * sample
							UpperCamelCase_:							Dict	=						[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
											UpperCamelCase_:							Any	=						self.get_scheduler_config()
											UpperCamelCase_:							Union[str, Any]	=						scheduler_class(**snake_case_      )
											scheduler.set_timesteps(snake_case_      )

											# copy over dummy past residuals (must be after setting timesteps)
											UpperCamelCase_:							Any	=						dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(snake_case_      )
															UpperCamelCase_:							str	=						scheduler_class.from_pretrained(snake_case_      )
															# copy over dummy past residuals
															new_scheduler.set_timesteps(snake_case_      )

															# copy over dummy past residual (must be after setting timesteps)
															UpperCamelCase_:							Any	=						dummy_past_residuals[: new_scheduler.config.solver_order]

											UpperCamelCase_:							Any	=						scheduler.step(snake_case_						,	snake_case_						,	snake_case_						,	**snake_case_      ).prev_sample
											UpperCamelCase_:							Union[str, Any]	=						new_scheduler.step(snake_case_						,	snake_case_						,	snake_case_						,	**snake_case_      ).prev_sample

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

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Optional[Any]=None						,	**snake_case_ :       List[str]      ):
							if scheduler is None:
											UpperCamelCase_:							Optional[Any]	=						self.scheduler_classes[0]
											UpperCamelCase_:							List[str]	=						self.get_scheduler_config(**snake_case_      )
											UpperCamelCase_:							Union[str, Any]	=						scheduler_class(**snake_case_      )

							UpperCamelCase_:							str	=						self.scheduler_classes[0]
							UpperCamelCase_:							List[str]	=						self.get_scheduler_config(**snake_case_      )
							UpperCamelCase_:							int	=						scheduler_class(**snake_case_      )

							UpperCamelCase_:							Tuple	=						10
							UpperCamelCase_:							int	=						self.dummy_model()
							UpperCamelCase_:							Union[str, Any]	=						self.dummy_sample_deter
							scheduler.set_timesteps(snake_case_      )

							for i, t in enumerate(scheduler.timesteps      ):
											UpperCamelCase_:							Dict	=						model(snake_case_						,	snake_case_      )
											UpperCamelCase_:							Optional[int]	=						scheduler.step(snake_case_						,	snake_case_						,	snake_case_      ).prev_sample

							return sample

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Optional[int]	=						dict(self.forward_default_kwargs      )

							UpperCamelCase_:							Any	=						kwargs.pop("""num_inference_steps"""						,	snake_case_      )

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

											UpperCamelCase_:							Optional[Any]	=						self.dummy_sample
											UpperCamelCase_:							int	=						0.1 * sample

											if num_inference_steps is not None and hasattr(snake_case_						,	"""set_timesteps"""      ):
															scheduler.set_timesteps(snake_case_      )
											elif num_inference_steps is not None and not hasattr(snake_case_						,	"""set_timesteps"""      ):
															UpperCamelCase_:							Optional[int]	=						num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											UpperCamelCase_:							Tuple	=						[residual + 0.2, residual + 0.15, residual + 0.10]
											UpperCamelCase_:							Optional[int]	=						dummy_past_residuals[: scheduler.config.solver_order]

											UpperCamelCase_:							Any	=						scheduler.timesteps[5]
											UpperCamelCase_:							Tuple	=						scheduler.timesteps[6]

											UpperCamelCase_:							str	=						scheduler.step(snake_case_						,	snake_case_						,	snake_case_						,	**snake_case_      ).prev_sample
											UpperCamelCase_:							Dict	=						scheduler.step(snake_case_						,	snake_case_						,	snake_case_						,	**snake_case_      ).prev_sample

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

			def       lowerCAmelCase__	(       self :       Any      ):
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							UpperCamelCase_:							List[str]	=						DEISMultistepScheduler(**self.get_scheduler_config()      )
							UpperCamelCase_:							Any	=						self.full_loop(scheduler=snake_case_      )
							UpperCamelCase_:							Dict	=						torch.mean(torch.abs(snake_case_      )      )

							assert abs(result_mean.item() - 0.2_3916      ) < 1e-3

							UpperCamelCase_:							Tuple	=						DPMSolverSinglestepScheduler.from_config(scheduler.config      )
							UpperCamelCase_:							Dict	=						DPMSolverMultistepScheduler.from_config(scheduler.config      )
							UpperCamelCase_:							int	=						UniPCMultistepScheduler.from_config(scheduler.config      )
							UpperCamelCase_:							Any	=						DEISMultistepScheduler.from_config(scheduler.config      )

							UpperCamelCase_:							Tuple	=						self.full_loop(scheduler=snake_case_      )
							UpperCamelCase_:							Optional[int]	=						torch.mean(torch.abs(snake_case_      )      )

							assert abs(result_mean.item() - 0.2_3916      ) < 1e-3

			def       lowerCAmelCase__	(       self :       int      ):
							for timesteps in [25, 50, 100, 999, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_      )

			def       lowerCAmelCase__	(       self :       int      ):
							self.check_over_configs(thresholding=snake_case_      )
							for order in [1, 2, 3]:
											for solver_type in ["logrho"]:
															for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "sample"]:
																							self.check_over_configs(
																							    thresholding=snake_case_						,	prediction_type=snake_case_						,	sample_max_value=snake_case_						,	algorithm_type="""deis"""						,	solver_order=snake_case_						,	solver_type=snake_case_						,	)

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

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							for algorithm_type in ["deis"]:
											for solver_type in ["logrho"]:
															for order in [1, 2, 3]:
																			for prediction_type in ["epsilon", "sample"]:
																							self.check_over_configs(
																							    solver_order=snake_case_						,	solver_type=snake_case_						,	prediction_type=snake_case_						,	algorithm_type=snake_case_						,	)
																							UpperCamelCase_:							List[str]	=						self.full_loop(
																							    solver_order=snake_case_						,	solver_type=snake_case_						,	prediction_type=snake_case_						,	algorithm_type=snake_case_						,	)
																							assert not torch.isnan(snake_case_      ).any(), "Samples have nan numbers"

			def       lowerCAmelCase__	(       self :       Dict      ):
							self.check_over_configs(lower_order_final=snake_case_      )
							self.check_over_configs(lower_order_final=snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
											self.check_over_forward(num_inference_steps=snake_case_						,	time_step=0      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.full_loop()
							UpperCamelCase_:							Dict	=						torch.mean(torch.abs(snake_case_      )      )

							assert abs(result_mean.item() - 0.2_3916      ) < 1e-3

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							int	=						self.full_loop(prediction_type="""v_prediction"""      )
							UpperCamelCase_:							List[str]	=						torch.mean(torch.abs(snake_case_      )      )

							assert abs(result_mean.item() - 0.091      ) < 1e-3




			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Dict	=						self.scheduler_classes[0]
							UpperCamelCase_:							List[Any]	=						self.get_scheduler_config(thresholding=snake_case_						,	dynamic_thresholding_ratio=0      )
							UpperCamelCase_:							List[str]	=						scheduler_class(**snake_case_      )

							UpperCamelCase_:							Optional[int]	=						10
							UpperCamelCase_:							str	=						self.dummy_model()
							UpperCamelCase_:							Dict	=						self.dummy_sample_deter.half()
							scheduler.set_timesteps(snake_case_      )

							for i, t in enumerate(scheduler.timesteps      ):
											UpperCamelCase_:							Optional[int]	=						model(snake_case_						,	snake_case_      )
											UpperCamelCase_:							Dict	=						scheduler.step(snake_case_						,	snake_case_						,	snake_case_      ).prev_sample

							assert sample.dtype == torch.floataa








import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = BarthezTokenizer
			__UpperCamelCase      :						str					      = BarthezTokenizerFast
			__UpperCamelCase      :						str					      = True
			__UpperCamelCase      :						List[Any]					      = True

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							super().setUp()

							UpperCamelCase_:							Tuple	=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""      )
							tokenizer.save_pretrained(self.tmpdirname      )
							tokenizer.save_pretrained(self.tmpdirname						,	legacy_format=snake_case_      )
							UpperCamelCase_:							Dict	=						tokenizer

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							str	=						"""<pad>"""
							UpperCamelCase_:							int	=						1

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

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						list(self.get_tokenizer().get_vocab().keys()      )

							self.assertEqual(vocab_keys[0]						,	"""<s>"""      )
							self.assertEqual(vocab_keys[1]						,	"""<pad>"""      )
							self.assertEqual(vocab_keys[-1]						,	"""<mask>"""      )
							self.assertEqual(len(snake_case_      )						,	10_1122      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							self.assertEqual(self.get_tokenizer().vocab_size						,	10_1122      )

			@require_torch
			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							UpperCamelCase_:							Union[str, Any]	=						[0, 57, 3018, 7_0307, 91, 2]

							UpperCamelCase_:							Union[str, Any]	=						self.tokenizer(
							    snake_case_						,	max_length=len(snake_case_      )						,	padding=snake_case_						,	truncation=snake_case_						,	return_tensors="""pt"""      )
							self.assertIsInstance(snake_case_						,	snake_case_      )

							self.assertEqual((2, 6)						,	batch.input_ids.shape      )
							self.assertEqual((2, 6)						,	batch.attention_mask.shape      )
							UpperCamelCase_:							Any	=						batch.input_ids.tolist()[0]
							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()
							UpperCamelCase_:							Union[str, Any]	=						self.get_rust_tokenizer()

							UpperCamelCase_:							str	=						"""I was born in 92000, and this is falsé."""

							UpperCamelCase_:							str	=						tokenizer.tokenize(snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.tokenize(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							int	=						tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							List[str]	=						self.get_rust_tokenizer()
							UpperCamelCase_:							Tuple	=						tokenizer.encode(snake_case_      )
							UpperCamelCase_:							Tuple	=						rust_tokenizer.encode(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )




			@slow
			def       lowerCAmelCase__	(       self :       int      ):
							# fmt: off
							UpperCamelCase_:							Optional[Any]	=						{"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 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], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], """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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# moussaKam/mbarthez is a french model. So we also use french texts.
							UpperCamelCase_:							str	=						[
							    """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=snake_case_						,	model_name="""moussaKam/mbarthez"""						,	revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""						,	sequences=snake_case_						,	)






