


'''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        __magic_name__  (					lowerCAmelCase						,		unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							HerbertTokenizer
     lowerCamelCase__						=							HerbertTokenizerFast
     lowerCamelCase__						=							True


     def lowerCAmelCase__     (      self		):


          '''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:
               __A  :					Dict						     =       f_data.read().replace("\n\n" ,    "\n"		).strip()

          __A  :					Dict						     =       [
              "<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>",
          ]
          __A  :					List[str]						     =       dict(zip(lowerCamelCase ,    range(len(lowerCamelCase		)		)		)		)
          __A  :					Optional[int]						     =       ["l o 123", "lo w 1456", "e r</w> 1789", ""]

          __A  :					Union[str, Any]						     =       os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES["vocab_file"]		)
          __A  :					Tuple						     =       os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES["merges_file"]		)
          with open(self.vocab_file ,    "w"		) as fp:
               fp.write(json.dumps(lowerCamelCase		)		)
          with open(self.merges_file ,    "w"		) as fp:
               fp.write("\n".join(lowerCamelCase		)		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       "lower newer"
          __A  :					Union[str, Any]						     =       "lower newer"
          return input_text, output_text


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[str]						     =       self.tokenizer_class(vocab_file=self.vocab_file ,    merges_file=self.merges_file		)

          __A  :					str						     =       "lower"
          __A  :					List[str]						     =       ["low", "er</w>"]
          __A  :					Dict						     =       tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase ,    lowerCamelCase		)

          __A  :					Tuple						     =       tokens + ["<unk>"]
          __A  :					Any						     =       [16, 17, 23]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		) ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          if not self.test_rust_tokenizer:
               return

          __A  :					List[Any]						     =       self.get_tokenizer()
          __A  :					Union[str, Any]						     =       self.get_rust_tokenizer()

          __A  :					Any						     =       "lower,newer"

          __A  :					List[str]						     =       tokenizer.tokenize(lowerCamelCase		)
          __A  :					Dict						     =       rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase ,    lowerCamelCase		)

          __A  :					Any						     =       tokenizer.encode(lowerCamelCase ,    add_special_tokens=lowerCamelCase		)
          __A  :					Optional[Any]						     =       rust_tokenizer.encode(lowerCamelCase ,    add_special_tokens=lowerCamelCase		)
          self.assertListEqual(lowerCamelCase ,    lowerCamelCase		)

          __A  :					int						     =       self.get_rust_tokenizer()
          __A  :					Tuple						     =       tokenizer.encode(lowerCamelCase		)
          __A  :					Optional[int]						     =       rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase ,    lowerCamelCase		)


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"		)

          __A  :					Union[str, Any]						     =       tokenizer.encode("konstruowanie sekwencji" ,    add_special_tokens=lowerCamelCase		)
          __A  :					Dict						     =       tokenizer.encode("konstruowanie wielu sekwencji" ,    add_special_tokens=lowerCamelCase		)

          __A  :					Union[str, Any]						     =       tokenizer.build_inputs_with_special_tokens(lowerCamelCase		)
          __A  :					List[str]						     =       tokenizer.build_inputs_with_special_tokens(lowerCamelCase ,    lowerCamelCase		)

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









'''simple docstring'''


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class        __magic_name__  (					unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							(ScoreSdeVeScheduler,)
     lowerCamelCase__						=							()


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       4
          __A  :					Union[str, Any]						     =       3
          __A  :					Optional[int]						     =       8
          __A  :					Dict						     =       8

          __A  :					Tuple						     =       torch.rand((batch_size, num_channels, height, width)		)

          return sample


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Tuple						     =       4
          __A  :					Dict						     =       3
          __A  :					str						     =       8
          __A  :					List[Any]						     =       8

          __A  :					Dict						     =       batch_size * num_channels * height * width
          __A  :					List[str]						     =       torch.arange(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       sample.reshape(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[Any]						     =       sample / num_elems
          __A  :					Dict						     =       sample.permute(3 ,    0 ,    1 ,    2		)

          return sample


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          def model(lowerCamelCase ,    lowerCamelCase ,    *lowerCamelCase		):
               return sample * t / (t + 1)

          return model


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       {
              "num_train_timesteps": 2000,
              "snr": 0.15,
              "sigma_min": 0.01,
              "sigma_max": 1348,
              "sampling_eps": 1E-5,
          }

          config.update(**lowerCamelCase		)
          return config


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       dict(self.forward_default_kwargs		)

          for scheduler_class in self.scheduler_classes:
               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[Any]						     =       0.1 * sample

               __A  :					Any						     =       self.get_scheduler_config(**lowerCamelCase		)
               __A  :					Tuple						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					List[str]						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[str]						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

               __A  :					Dict						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[Any]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       dict(self.forward_default_kwargs		)
          kwargs.update(lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					Optional[Any]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               __A  :					str						     =       self.get_scheduler_config()
               __A  :					Optional[Any]						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					Dict						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					str						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

               __A  :					List[str]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01] ,    [1, 100, 1000]		):
               self.check_over_configs(sigma_min=lowerCamelCase ,    sigma_max=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					Dict						     =       self.scheduler_classes[0]
          __A  :					str						     =       self.get_scheduler_config()
          __A  :					List[Any]						     =       scheduler_class(**lowerCamelCase		)

          __A  :					Union[str, Any]						     =       3

          __A  :					Union[str, Any]						     =       self.dummy_model()
          __A  :					Union[str, Any]						     =       self.dummy_sample_deter

          scheduler.set_sigmas(lowerCamelCase		)
          scheduler.set_timesteps(lowerCamelCase		)
          __A  :					Optional[int]						     =       torch.manual_seed(0		)

          for i, t in enumerate(scheduler.timesteps		):
               __A  :					int						     =       scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps		):
                    with torch.no_grad():
                         __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)
                    __A  :					Optional[int]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		).prev_sample

               with torch.no_grad():
                    __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		)
               __A     ,__A  :					int						     =       output.prev_sample, output.prev_sample_mean

          __A  :					Union[str, Any]						     =       torch.sum(torch.abs(lowerCamelCase		)		)
          __A  :					str						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert np.isclose(result_sum.item() ,    143_7275_8528.0		)
          assert np.isclose(result_mean.item() ,    1871_4530.0		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					List[Any]						     =       kwargs.pop("num_inference_steps" ,    lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					int						     =       self.get_scheduler_config()
               __A  :					Any						     =       scheduler_class(**lowerCamelCase		)

               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               if num_inference_steps is not None and hasattr(lowerCamelCase ,    "set_timesteps"		):
                    scheduler.set_timesteps(lowerCamelCase		)
               elif num_inference_steps is not None and not hasattr(lowerCamelCase ,    "set_timesteps"		):
                    __A  :					Any						     =       num_inference_steps

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    0 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       scheduler.step_pred(lowerCamelCase ,    1 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

