




'''simple docstring'''







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class       _a  (     __a			):
       __a    :					Optional[Any]           = (DPMSolverMultistepScheduler,)
       __a    :					int           = (("""num_inference_steps""", 25),)
       def    A				(     self						:     Optional[int]       ,    **lowercase						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		{
               '''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''',
               '''lower_order_final''': False,
               '''lambda_min_clipped''': -float('''inf'''    ),
               '''variance_type''': None,
           }

           config.update(**lowercase    )
           return config
       def    A				(     self						:     int       ,    lowercase						:     Union[str, Any]=0       ,    **lowercase						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		dict(self.forward_default_kwargs    )
           UpperCAmelCase      	=		kwargs.pop('''num_inference_steps'''       ,    lowercase    )
           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(**lowercase    )
               UpperCAmelCase      	=		scheduler_class(**lowercase    )
               scheduler.set_timesteps(lowercase    )
               # copy over dummy past residuals
               UpperCAmelCase      	=		dummy_past_residuals[: scheduler.config.solver_order]

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

               UpperCAmelCase	,					UpperCAmelCase      	=		sample, sample
               for t in range(lowercase       ,    time_step + scheduler.config.solver_order + 1    ):
                   UpperCAmelCase      	=		scheduler.step(lowercase       ,    lowercase       ,    lowercase       ,    **lowercase    ).prev_sample
                   UpperCAmelCase      	=		new_scheduler.step(lowercase       ,    lowercase       ,    lowercase       ,    **lowercase    ).prev_sample

                   assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           pass
       def    A				(     self						:     Any       ,    lowercase						:     Optional[int]=0       ,    **lowercase						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		dict(self.forward_default_kwargs    )
           UpperCAmelCase      	=		kwargs.pop('''num_inference_steps'''       ,    lowercase    )
           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(**lowercase    )
               scheduler.set_timesteps(lowercase    )

               # 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(lowercase    )
                   UpperCAmelCase      	=		scheduler_class.from_pretrained(lowercase    )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(lowercase    )

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

               UpperCAmelCase      	=		scheduler.step(lowercase       ,    lowercase       ,    lowercase       ,    **lowercase    ).prev_sample
               UpperCAmelCase      	=		new_scheduler.step(lowercase       ,    lowercase       ,    lowercase       ,    **lowercase    ).prev_sample

               assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"
       def    A				(     self						:     Dict       ,    lowercase						:     Tuple=None       ,    **lowercase						:     List[Any]    ):



           '''simple docstring'''

           if scheduler is None:
               UpperCAmelCase      	=		self.scheduler_classes[0]
               UpperCAmelCase      	=		self.get_scheduler_config(**lowercase    )
               UpperCAmelCase      	=		scheduler_class(**lowercase    )

           UpperCAmelCase      	=		10
           UpperCAmelCase      	=		self.dummy_model()
           UpperCAmelCase      	=		self.dummy_sample_deter
           scheduler.set_timesteps(lowercase    )

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

           return sample
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		dict(self.forward_default_kwargs    )

           UpperCAmelCase      	=		kwargs.pop('''num_inference_steps'''       ,    lowercase    )

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase      	=		self.get_scheduler_config()
               UpperCAmelCase      	=		scheduler_class(**lowercase    )

               UpperCAmelCase      	=		self.dummy_sample
               UpperCAmelCase      	=		0.1 * sample

               if num_inference_steps is not None and hasattr(lowercase       ,    '''set_timesteps'''    ):
                   scheduler.set_timesteps(lowercase    )
               elif num_inference_steps is not None and not hasattr(lowercase       ,    '''set_timesteps'''    ):
                   UpperCAmelCase      	=		num_inference_steps

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

               UpperCAmelCase      	=		scheduler.timesteps[5]
               UpperCAmelCase      	=		scheduler.timesteps[6]

               UpperCAmelCase      	=		scheduler.step(lowercase       ,    lowercase       ,    lowercase       ,    **lowercase    ).prev_sample
               UpperCAmelCase      	=		scheduler.step(lowercase       ,    lowercase       ,    lowercase       ,    **lowercase    ).prev_sample

               self.assertEqual(output_a.shape       ,    sample.shape    )
               self.assertEqual(output_a.shape       ,    output_a.shape    )
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           for timesteps in [25, 50, 100, 999, 1_000]:
               self.check_over_configs(num_train_timesteps=lowercase    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           self.check_over_configs(thresholding=lowercase    )
           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=lowercase       ,    prediction_type=lowercase       ,    sample_max_value=lowercase       ,    algorithm_type='''dpmsolver++'''       ,    solver_order=lowercase       ,    solver_type=lowercase       ,    )
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowercase    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                   for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                           if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                               if order == 3:
                                   continue
                           else:
                               self.check_over_configs(
                                   solver_order=lowercase       ,    solver_type=lowercase       ,    prediction_type=lowercase       ,    algorithm_type=lowercase       ,    )
                           UpperCAmelCase      	=		self.full_loop(
                               solver_order=lowercase       ,    solver_type=lowercase       ,    prediction_type=lowercase       ,    algorithm_type=lowercase       ,    )
                           assert not torch.isnan(lowercase    ).any(), "Samples have nan numbers"
       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

           self.check_over_configs(lower_order_final=lowercase    )
           self.check_over_configs(lower_order_final=lowercase    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           self.check_over_configs(lambda_min_clipped=-float('''inf'''    )    )
           self.check_over_configs(lambda_min_clipped=-5.1    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           self.check_over_configs(variance_type=lowercase    )
           self.check_over_configs(variance_type='''learned_range'''    )
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
               self.check_over_forward(num_inference_steps=lowercase       ,    time_step=0    )
       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.full_loop()
           UpperCAmelCase      	=		torch.mean(torch.abs(lowercase    )    )

           assert abs(result_mean.item() - 0.3301    ) < 1E-3
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.full_loop(thresholding=lowercase       ,    dynamic_thresholding_ratio=0.87       ,    sample_max_value=0.5    )
           UpperCAmelCase      	=		torch.mean(torch.abs(lowercase    )    )

           assert abs(result_mean.item() - 1.1364    ) < 1E-3
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.full_loop(prediction_type='''v_prediction'''    )
           UpperCAmelCase      	=		torch.mean(torch.abs(lowercase    )    )

           assert abs(result_mean.item() - 0.2251    ) < 1E-3
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.full_loop(prediction_type='''v_prediction'''       ,    use_karras_sigmas=lowercase    )
           UpperCAmelCase      	=		torch.mean(torch.abs(lowercase    )    )

           assert abs(result_mean.item() - 0.2096    ) < 1E-3
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
           UpperCAmelCase      	=		self.full_loop(scheduler=lowercase    )
           UpperCAmelCase      	=		torch.mean(torch.abs(lowercase    )    )

           assert abs(result_mean.item() - 0.3301    ) < 1E-3

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

           UpperCAmelCase      	=		self.full_loop(scheduler=lowercase    )
           UpperCAmelCase      	=		torch.mean(torch.abs(lowercase    )    )

           assert abs(result_mean.item() - 0.3301    ) < 1E-3
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.scheduler_classes[0]
           UpperCAmelCase      	=		self.get_scheduler_config(thresholding=lowercase       ,    dynamic_thresholding_ratio=0    )
           UpperCAmelCase      	=		scheduler_class(**lowercase    )

           UpperCAmelCase      	=		10
           UpperCAmelCase      	=		self.dummy_model()
           UpperCAmelCase      	=		self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowercase    )

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

           assert sample.dtype == torch.floataa







       def    A				(     self						:     int       ,    **lowercase						:     List[Any]    ):



           '''simple docstring'''

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase      	=		self.get_scheduler_config(**lowercase    )
               UpperCAmelCase      	=		scheduler_class(**lowercase    )

               scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
               assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps










'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       _a  (     __a							,							unittest.TestCase			):
       __a    :					str           = HerbertTokenizer
       __a    :					Optional[Any]           = HerbertTokenizerFast
       __a    :					Any           = True
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           super().setUp()

           # Use a simpler test file without japanese/chinese characters
           with open(f"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"       ,    encoding='''utf-8'''    ) as f_data:
               UpperCAmelCase      	=		f_data.read().replace('''\n\n'''       ,    '''\n'''    ).strip()

           UpperCAmelCase      	=		[
               '''<s>''',
               '''</s>''',
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''w</w>''',
               '''r</w>''',
               '''t</w>''',
               '''lo''',
               '''low''',
               '''er</w>''',
               '''low</w>''',
               '''lowest</w>''',
               '''newer</w>''',
               '''wider</w>''',
               ''',</w>''',
               '''<unk>''',
           ]
           UpperCAmelCase      	=		dict(zip(lowercase       ,    range(len(lowercase    )    )    )    )
           UpperCAmelCase      	=		['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''merges_file''']    )
           with open(self.vocab_file       ,    '''w'''    ) as fp:
               fp.write(json.dumps(lowercase    )    )
           with open(self.merges_file       ,    '''w'''    ) as fp:
               fp.write('''\n'''.join(lowercase    )    )
       def    A				(     self						:     List[Any]       ,    lowercase						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		'''lower newer'''
           return input_text, output_text
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(vocab_file=self.vocab_file       ,    merges_file=self.merges_file    )

           UpperCAmelCase      	=		'''lower'''
           UpperCAmelCase      	=		['''low''', '''er</w>''']
           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokens + ['''<unk>''']
           UpperCAmelCase      	=		[16, 17, 23]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    lowercase    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           if not self.test_rust_tokenizer:
               return

           UpperCAmelCase      	=		self.get_tokenizer()
           UpperCAmelCase      	=		self.get_rust_tokenizer()

           UpperCAmelCase      	=		'''lower,newer'''

           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		self.get_rust_tokenizer()
           UpperCAmelCase      	=		tokenizer.encode(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )







       @slow
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''    )

           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie sekwencji'''       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie wielu sekwencji'''       ,    add_special_tokens=lowercase    )

           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase    )
           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase       ,    lowercase    )

           assert encoded_sentence == [0] + text + [2]
           assert encoded_pair == [0] + text + [2] + text_a + [2]




