






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    (DPMSolverSinglestepScheduler,)
      A          =    (('''num_inference_steps''', 25),)
      def 			snake_case_      (   self    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "solver_order": 2,
              "prediction_type": "epsilon",
              "thresholding": False,
              "sample_max_value": 1.0,
              "algorithm_type": "dpmsolver++",
              "solver_type": "midpoint",
              "lambda_min_clipped": -float("inf"		),
              "variance_type": None,
          }

          config.update(**_lowerCAmelCase		)
          return config
      def 			snake_case_      (   self    ,	_lowerCAmelCase=0    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				dict(self.forward_default_kwargs		)
          lowerCAmelCase__   :Optional[Any]      							=				kwargs.pop("num_inference_steps"    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				self.dummy_sample
          lowerCAmelCase__   :Tuple      							=				0.1 * sample
          lowerCAmelCase__   :Optional[Any]      							=				[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__   :Any      							=				self.get_scheduler_config(**_lowerCAmelCase		)
              lowerCAmelCase__   :List[Any]      							=				scheduler_class(**_lowerCAmelCase		)
              scheduler.set_timesteps(_lowerCAmelCase		)
              # copy over dummy past residuals
              lowerCAmelCase__   :Tuple      							=				dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_lowerCAmelCase		)
                  lowerCAmelCase__   :Union[str, Any]      							=				scheduler_class.from_pretrained(_lowerCAmelCase		)
                  new_scheduler.set_timesteps(_lowerCAmelCase		)
                  # copy over dummy past residuals
                  lowerCAmelCase__   :List[str]      							=				dummy_past_residuals[: new_scheduler.config.solver_order]

              lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				sample, sample
              for t in range(_lowerCAmelCase    ,	time_step + scheduler.config.solver_order + 1		):
                  lowerCAmelCase__   :Any      							=				scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample
                  lowerCAmelCase__   :Optional[Any]      							=				new_scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

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



          '''simple docstring'''
          pass
      def 			snake_case_      (   self    ,	_lowerCAmelCase=0    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				dict(self.forward_default_kwargs		)
          lowerCAmelCase__   :Tuple      							=				kwargs.pop("num_inference_steps"    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				self.dummy_sample
          lowerCAmelCase__   :List[Any]      							=				0.1 * sample
          lowerCAmelCase__   :int      							=				[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__   :str      							=				self.get_scheduler_config()
              lowerCAmelCase__   :str      							=				scheduler_class(**_lowerCAmelCase		)
              scheduler.set_timesteps(_lowerCAmelCase		)

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_lowerCAmelCase		)
                  lowerCAmelCase__   :Union[str, Any]      							=				scheduler_class.from_pretrained(_lowerCAmelCase		)
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(_lowerCAmelCase		)

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

              lowerCAmelCase__   :int      							=				scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample
              lowerCAmelCase__   :str      							=				new_scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

              assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
      def 			snake_case_      (   self    ,	_lowerCAmelCase=None    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          if scheduler is None:
              lowerCAmelCase__   :Any      							=				self.scheduler_classes[0]
              lowerCAmelCase__   :Union[str, Any]      							=				self.get_scheduler_config(**_lowerCAmelCase		)
              lowerCAmelCase__   :Optional[int]      							=				scheduler_class(**_lowerCAmelCase		)

          lowerCAmelCase__   :List[Any]      							=				self.scheduler_classes[0]
          lowerCAmelCase__   :Dict      							=				self.get_scheduler_config(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				scheduler_class(**_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				10
          lowerCAmelCase__   :Optional[Any]      							=				self.dummy_model()
          lowerCAmelCase__   :List[Any]      							=				self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
              lowerCAmelCase__   :Tuple      							=				model(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample

          return sample
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
          lowerCAmelCase__   :Tuple      							=				50
          lowerCAmelCase__   :str      							=				self.dummy_model()
          lowerCAmelCase__   :List[Any]      							=				self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCAmelCase		)

          # make sure that the first t is uneven
          for i, t in enumerate(scheduler.timesteps[3:]		):
              lowerCAmelCase__   :Optional[Any]      							=				model(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Optional[Any]      							=				scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample

          lowerCAmelCase__   :Optional[Any]      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2574		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for timesteps in [25, 50, 100, 999, 1_000]:
              self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          lowerCAmelCase__   :int      							=				DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
          lowerCAmelCase__   :Tuple      							=				self.full_loop(scheduler=_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1e-3

          lowerCAmelCase__   :Optional[int]      							=				DEISMultistepScheduler.from_config(scheduler.config		)
          lowerCAmelCase__   :Dict      							=				DPMSolverMultistepScheduler.from_config(scheduler.config		)
          lowerCAmelCase__   :Optional[int]      							=				UniPCMultistepScheduler.from_config(scheduler.config		)
          lowerCAmelCase__   :Tuple      							=				DPMSolverSinglestepScheduler.from_config(scheduler.config		)

          lowerCAmelCase__   :Tuple      							=				self.full_loop(scheduler=_lowerCAmelCase		)
          lowerCAmelCase__   :str      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.check_over_configs(thresholding=_lowerCAmelCase		)
          for order in [1, 2, 3]:
              for solver_type in ["midpoint", "heun"]:
                  for threshold in [0.5, 1.0, 2.0]:
                      for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=_lowerCAmelCase    ,	prediction_type=_lowerCAmelCase    ,	sample_max_value=_lowerCAmelCase    ,	algorithm_type="dpmsolver++"    ,	solver_order=_lowerCAmelCase    ,	solver_type=_lowerCAmelCase    ,	)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for algorithm_type in ["dpmsolver", "dpmsolver++"]:
              for solver_type in ["midpoint", "heun"]:
                  for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              solver_order=_lowerCAmelCase    ,	solver_type=_lowerCAmelCase    ,	prediction_type=_lowerCAmelCase    ,	algorithm_type=_lowerCAmelCase    ,	)
                          lowerCAmelCase__   :Union[str, Any]      							=				self.full_loop(
                              solver_order=_lowerCAmelCase    ,	solver_type=_lowerCAmelCase    ,	prediction_type=_lowerCAmelCase    ,	algorithm_type=_lowerCAmelCase    ,	)
                          assert not torch.isnan(_lowerCAmelCase		).any(), "Samples have nan numbers"
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.check_over_configs(lower_order_final=_lowerCAmelCase		)
          self.check_over_configs(lower_order_final=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
          self.check_over_configs(lambda_min_clipped=-5.1		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.check_over_configs(variance_type=_lowerCAmelCase		)
          self.check_over_configs(variance_type="learned_range"		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
              self.check_over_forward(num_inference_steps=_lowerCAmelCase    ,	time_step=0		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				self.full_loop()
          lowerCAmelCase__   :int      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.full_loop(use_karras_sigmas=_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2248		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				self.full_loop(prediction_type="v_prediction"		)
          lowerCAmelCase__   :Any      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.1453		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.full_loop(prediction_type="v_prediction"    ,	use_karras_sigmas=_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.0649		) < 1e-3



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				self.scheduler_classes[0]
          lowerCAmelCase__   :Union[str, Any]      							=				self.get_scheduler_config(thresholding=_lowerCAmelCase    ,	dynamic_thresholding_ratio=0		)
          lowerCAmelCase__   :Optional[int]      							=				scheduler_class(**_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				10
          lowerCAmelCase__   :List[Any]      							=				self.dummy_model()
          lowerCAmelCase__   :List[Any]      							=				self.dummy_sample_deter.half()
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
              lowerCAmelCase__   :Dict      							=				model(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :List[str]      							=				scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample

          assert sample.dtype == torch.floataa











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


_a    :   List[Any]        =			get_tests_dir("""fixtures/test_sentencepiece.model""")
_a    :   Dict        =			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_a    :   Optional[Any]        =			"""pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    CamembertTokenizer
      A          =    CamembertTokenizerFast
      A          =    True
      A          =    True
      def 			snake_case_      (   self		):



          '''simple docstring'''
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowerCAmelCase__   :List[str]      							=				CamembertTokenizer(_lowerCAmelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				"<pad>"
          lowerCAmelCase__   :Union[str, Any]      							=				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		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				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		)    ,	1_004		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.assertEqual(self.get_tokenizer().vocab_size    ,	1_005		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				CamembertTokenizer(_lowerCAmelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
          lowerCAmelCase__   :Tuple      							=				CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

          lowerCAmelCase__   :List[str]      							=				"I was born in 92000, and this is falsé."

          lowerCAmelCase__   :Any      							=				tokenizer.encode(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				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)
          lowerCAmelCase__   :Union[str, Any]      							=				tokenizer.convert_ids_to_tokens(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				rust_tokenizer.tokenize(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          if not self.test_rust_tokenizer:
              return

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

          lowerCAmelCase__   :List[str]      							=				"I was born in 92000, and this is falsé."

          lowerCAmelCase__   :int      							=				tokenizer.tokenize(_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				rust_tokenizer.tokenize(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Union[str, Any]      							=				tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Dict      							=				self.get_rust_tokenizer()
          lowerCAmelCase__   :Tuple      							=				tokenizer.encode(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # fmt: off
          lowerCAmelCase__   :str      							=				{"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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.
          lowerCAmelCase__   :Tuple      							=				[
              "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    ,	)



