
"""simple docstring"""

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	(IPNDMScheduler,)
      __SCREAMING_SNAKE_CASE   :							int							      =	(("num_inference_steps", 5_0),)




      def     a_      (							self   ,       **lowercase_							)						->					Dict:
          UpperCAmelCase									=					{'num_train_timesteps': 1_0_0_0}
          config.update(**lowercase_							)
          return config




      def     a_      (							self   ,       lowercase_=0   ,       **lowercase_							)						->					Union[str, Any]:
          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 + 0.1_5, residual + 0.1, residual + 0.0_5]

          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[:]

              if time_step is None:
                  UpperCAmelCase									=					scheduler.timesteps[len(scheduler.timesteps							) // 2]

              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[:]

              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"

              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							)						->					int:
          pass




      def     a_      (							self   ,       lowercase_=0   ,       **lowercase_							)						->					int:
          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 + 0.1_5, residual + 0.1, residual + 0.0_5]

          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[:]

              if time_step is None:
                  UpperCAmelCase									=					scheduler.timesteps[len(scheduler.timesteps							) // 2]

              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[:]

              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"

              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   ,       **lowercase_							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(**lowercase_							)
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					1_0
          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

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

          return sample




      def     a_      (							self							)						->					List[Any]:
          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 + 0.1_5, residual + 0.1, residual + 0.0_5]
              UpperCAmelCase									=					dummy_past_residuals[:]

              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							)

              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							)						->					Optional[Any]:
          for timesteps in [1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=lowercase_   ,       time_step=lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          for t, num_inference_steps in zip([1, 5, 1_0]   ,       [1_0, 5_0, 1_0_0]							):
              self.check_over_forward(num_inference_steps=lowercase_   ,       time_step=lowercase_							)




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					self.full_loop()
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_mean.item() - 2_5_4_0_5_2_9							) < 1_0





"""simple docstring"""

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							str							      =	CTRLTokenizer
      __SCREAMING_SNAKE_CASE   :							int							      =	False
      __SCREAMING_SNAKE_CASE   :							Optional[Any]							      =	False




      def     a_      (							self							)						->					Dict:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCAmelCase									=					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
          UpperCAmelCase									=					dict(zip(lowercase_   ,       range(len(lowercase_							)							)							)							)
          UpperCAmelCase									=					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
          UpperCAmelCase									=					{'unk_token': '<unk>'}

          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'   ,       encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)
          with open(self.merges_file   ,       'w'   ,       encoding='utf-8'							) as fp:
              fp.write('\n'.join(lowercase_							)							)




      def     a_      (							self   ,       **lowercase_							)						->					Tuple:
          kwargs.update(self.special_tokens_map							)
          return CTRLTokenizer.from_pretrained(self.tmpdirname   ,       **lowercase_							)




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          UpperCAmelCase									=					'adapt react readapt apt'
          UpperCAmelCase									=					'adapt react readapt apt'
          return input_text, output_text




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					CTRLTokenizer(self.vocab_file   ,       self.merges_file   ,       **self.special_tokens_map							)
          UpperCAmelCase									=					'adapt react readapt apt'
          UpperCAmelCase									=					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
          UpperCAmelCase									=					tokenizer.tokenize(lowercase_							)
          self.assertListEqual(lowercase_   ,       lowercase_							)

          UpperCAmelCase									=					tokens + [tokenizer.unk_token]

          UpperCAmelCase									=					[0, 1, 2, 4, 5, 1, 0, 3, 6]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)   ,       lowercase_							)



