"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	int  				=				(DPMSolverSinglestepScheduler,)
    _snake_case  :	Any  				=				(('num_inference_steps', 25),)


    def   lowerCAmelCase_	(       self						:	int ,					**__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   {
          """num_train_timesteps""": 1000,
          """beta_start""": 0.0_001,
          """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   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	str=0 ,					**__lowerCAmelCase						:	Any       ):
      _UpperCAmelCase    			=   dict(self.forward_default_kwargs       )
      _UpperCAmelCase    			=   kwargs.pop("""num_inference_steps""" ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.dummy_sample
      _UpperCAmelCase    			=   0.1 * sample
      _UpperCAmelCase    			=   [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        _UpperCAmelCase    			=   self.get_scheduler_config(**__lowerCAmelCase       )
        _UpperCAmelCase    			=   scheduler_class(**__lowerCAmelCase       )
        scheduler.set_timesteps(__lowerCAmelCase       )
        # copy over dummy past residuals
        _UpperCAmelCase    			=   dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(__lowerCAmelCase       )
          _UpperCAmelCase    			=   scheduler_class.from_pretrained(__lowerCAmelCase       )
          new_scheduler.set_timesteps(__lowerCAmelCase       )
          # copy over dummy past residuals
          _UpperCAmelCase    			=   dummy_past_residuals[: new_scheduler.config.solver_order]

        _UpperCAmelCase			,					_UpperCAmelCase    			=   sample, sample
        for t in range(__lowerCAmelCase ,					time_step + scheduler.config.solver_order + 1       ):
          _UpperCAmelCase    			=   scheduler.step(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					**__lowerCAmelCase       ).prev_sample
          _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      pass


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	str=0 ,					**__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   dict(self.forward_default_kwargs       )
      _UpperCAmelCase    			=   kwargs.pop("""num_inference_steps""" ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.dummy_sample
      _UpperCAmelCase    			=   0.1 * sample
      _UpperCAmelCase    			=   [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        _UpperCAmelCase    			=   self.get_scheduler_config()
        _UpperCAmelCase    			=   scheduler_class(**__lowerCAmelCase       )
        scheduler.set_timesteps(__lowerCAmelCase       )

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

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(__lowerCAmelCase       )
          _UpperCAmelCase    			=   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)
          _UpperCAmelCase    			=   dummy_past_residuals[: new_scheduler.config.solver_order]

        _UpperCAmelCase    			=   scheduler.step(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					**__lowerCAmelCase       ).prev_sample
        _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Any=None ,					**__lowerCAmelCase						:	Any       ):
      if scheduler is None:
        _UpperCAmelCase    			=   self.scheduler_classes[0]
        _UpperCAmelCase    			=   self.get_scheduler_config(**__lowerCAmelCase       )
        _UpperCAmelCase    			=   scheduler_class(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   self.scheduler_classes[0]
      _UpperCAmelCase    			=   self.get_scheduler_config(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   scheduler_class(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   10
      _UpperCAmelCase    			=   self.dummy_model()
      _UpperCAmelCase    			=   self.dummy_sample_deter
      scheduler.set_timesteps(__lowerCAmelCase       )

      for i, t in enumerate(scheduler.timesteps       ):
        _UpperCAmelCase    			=   model(__lowerCAmelCase ,					__lowerCAmelCase       )
        _UpperCAmelCase    			=   scheduler.step(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       ).prev_sample

      return sample


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
      _UpperCAmelCase    			=   50
      _UpperCAmelCase    			=   self.dummy_model()
      _UpperCAmelCase    			=   self.dummy_sample_deter
      scheduler.set_timesteps(__lowerCAmelCase       )

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

      _UpperCAmelCase    			=   torch.mean(torch.abs(__lowerCAmelCase       )       )

      assert abs(result_mean.item() - 0.2_574       ) < 1e-3


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


    def   lowerCAmelCase_	(       self						:	Any       ):
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      _UpperCAmelCase    			=   DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
      _UpperCAmelCase    			=   self.full_loop(scheduler=__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.mean(torch.abs(__lowerCAmelCase       )       )

      assert abs(result_mean.item() - 0.2_791       ) < 1e-3

      _UpperCAmelCase    			=   DEISMultistepScheduler.from_config(scheduler.config       )
      _UpperCAmelCase    			=   DPMSolverMultistepScheduler.from_config(scheduler.config       )
      _UpperCAmelCase    			=   UniPCMultistepScheduler.from_config(scheduler.config       )
      _UpperCAmelCase    			=   DPMSolverSinglestepScheduler.from_config(scheduler.config       )

      _UpperCAmelCase    			=   self.full_loop(scheduler=__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.mean(torch.abs(__lowerCAmelCase       )       )

      assert abs(result_mean.item() - 0.2_791       ) < 1e-3


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      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   lowerCAmelCase_	(       self						:	int       ):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      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 ,					)
              _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Optional[int]       ):
      self.check_over_configs(lower_order_final=__lowerCAmelCase       )
      self.check_over_configs(lower_order_final=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      self.check_over_configs(lambda_min_clipped=-float("""inf"""       )       )
      self.check_over_configs(lambda_min_clipped=-5.1       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      self.check_over_configs(variance_type=__lowerCAmelCase       )
      self.check_over_configs(variance_type="""learned_range"""       )


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


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   self.full_loop()
      _UpperCAmelCase    			=   torch.mean(torch.abs(__lowerCAmelCase       )       )

      assert abs(result_mean.item() - 0.2_791       ) < 1e-3


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   self.full_loop(use_karras_sigmas=__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.mean(torch.abs(__lowerCAmelCase       )       )

      assert abs(result_mean.item() - 0.2_248       ) < 1e-3


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.full_loop(prediction_type="""v_prediction"""       )
      _UpperCAmelCase    			=   torch.mean(torch.abs(__lowerCAmelCase       )       )

      assert abs(result_mean.item() - 0.1_453       ) < 1e-3


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   self.full_loop(prediction_type="""v_prediction""" ,					use_karras_sigmas=__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.mean(torch.abs(__lowerCAmelCase       )       )

      assert abs(result_mean.item() - 0.0_649       ) < 1e-3


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.scheduler_classes[0]
      _UpperCAmelCase    			=   self.get_scheduler_config(thresholding=__lowerCAmelCase ,					dynamic_thresholding_ratio=0       )
      _UpperCAmelCase    			=   scheduler_class(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   10
      _UpperCAmelCase    			=   self.dummy_model()
      _UpperCAmelCase    			=   self.dummy_sample_deter.half()
      scheduler.set_timesteps(__lowerCAmelCase       )

      for i, t in enumerate(scheduler.timesteps       ):
        _UpperCAmelCase    			=   model(__lowerCAmelCase ,					__lowerCAmelCase       )
        _UpperCAmelCase    			=   scheduler.step(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       ).prev_sample

      assert sample.dtype == torch.floataa

"""simple docstring"""


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Dict  				=				MgpstrTokenizer
    _snake_case  :	Any  				=				False
    _snake_case  :	str  				=				{}
    _snake_case  :	Dict  				=				False


    def   lowerCAmelCase_	(       self						:	int       ):
      super().setUp()

      # fmt: off
      _UpperCAmelCase    			=   ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
      # fmt: on
      _UpperCAmelCase    			=   dict(zip(__lowerCAmelCase ,					range(len(__lowerCAmelCase       )       )       )       )

      _UpperCAmelCase    			=   os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]       )
      with open(self.vocab_file ,					"""w""" ,					encoding="""utf-8"""       ) as fp:
        fp.write(json.dumps(__lowerCAmelCase       ) + """\n"""       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					**__lowerCAmelCase						:	Dict       ):
      return MgpstrTokenizer.from_pretrained(self.tmpdirname ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   """tester"""
      _UpperCAmelCase    			=   """tester"""
      return input_text, output_text


    @unittest.skip("""MGP-STR always lower cases letters."""       )
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      pass


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.get_tokenizers(do_lower_case=__lowerCAmelCase       )
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase    			=   """[SPECIAL_TOKEN]"""

          tokenizer.add_special_tokens({"""cls_token""": special_token}       )
          _UpperCAmelCase    			=   tokenizer.encode([special_token] ,					add_special_tokens=__lowerCAmelCase       )
          self.assertEqual(len(__lowerCAmelCase       ) ,					1       )

          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
          self.assertTrue(special_token not in decoded       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase			,					_UpperCAmelCase    			=   self.get_input_output_texts(__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.tokenize(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.convert_tokens_to_ids(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase       )
          self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.convert_ids_to_tokens(__lowerCAmelCase       )
          self.assertNotEqual(len(__lowerCAmelCase       ) ,					0       )
          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase       )
          self.assertIsInstance(__lowerCAmelCase ,					__lowerCAmelCase       )

          self.assertEqual(text_a.replace(""" """ ,					""""""       ) ,					__lowerCAmelCase       )


    @unittest.skip("""MGP-STR tokenizer only handles one sequence."""       )
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      pass


    @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""       )
    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      pass
