

import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      int       =  LongformerTokenizer
  SCREAMING_SNAKE_CASE    :      int       =  True
  SCREAMING_SNAKE_CASE    :      List[str]       =  LongformerTokenizerFast
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  True

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE_				:  int           =     [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     dict(zip(_SCREAMING_SNAKE_CASE ,					range(len(_SCREAMING_SNAKE_CASE					)					)					)					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {'unk_token': '<unk>'}

        SCREAMING_SNAKE_CASE_				:  str           =     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['vocab_file']					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     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(_SCREAMING_SNAKE_CASE					) + '\n'					)
        with open(self.merges_file ,					'w' ,					encoding='utf-8'					) as fp:
              fp.write('\n'.join(_SCREAMING_SNAKE_CASE					)					)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        kwargs.update(self.special_tokens_map					)
        return self.tokenizer_class.from_pretrained(self.tmpdirname ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        kwargs.update(self.special_tokens_map					)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     'lower newer'
        SCREAMING_SNAKE_CASE_				:  List[str]           =     'lower newer'
        return input_text, output_text

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.tokenizer_class(self.vocab_file ,					self.merges_file ,					**self.special_tokens_map					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'lower newer'
        SCREAMING_SNAKE_CASE_				:  int           =     ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer.tokenize(_SCREAMING_SNAKE_CASE					)  # , add_prefix_space=True)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE_				:  str           =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					) ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.get_tokenizer()

        self.assertListEqual(tokenizer.encode('Hello world!' ,					add_special_tokens=_SCREAMING_SNAKE_CASE					) ,					[0, 3_1414, 232, 328, 2]					)
        self.assertListEqual(
            tokenizer.encode('Hello world! cécé herlolip 418' ,					add_special_tokens=_SCREAMING_SNAKE_CASE					) ,					[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2] ,					)

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.encode('sequence builders' ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer.encode('multi-sequence build' ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.encode(
            'sequence builders' ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.encode(
            'sequence builders' ,					'multi-sequence build' ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_				:  Dict           =     'Encode this sequence.'
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.byte_encoder[' '.encode('utf-8'					)[0]]

        # Testing encoder arguments
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
        self.assertNotEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        tokenizer.add_special_tokens({'bos_token': '<s>'}					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer.encode(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
        self.assertNotEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        # Testing spaces after special tokens
        SCREAMING_SNAKE_CASE_				:  str           =     '<mask>'
        tokenizer.add_special_tokens(
            {'mask_token': AddedToken(_SCREAMING_SNAKE_CASE ,					lstrip=_SCREAMING_SNAKE_CASE ,					rstrip=_SCREAMING_SNAKE_CASE					)}					)  # mask token has a left space
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     'Encode <mask> sequence'
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     'Encode <mask>sequence'

        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     encoded.index(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     encoded.index(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
        self.assertNotEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     'A, <mask> AllenNLP sentence.'
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					return_token_type_ids=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					return_token_type_ids=_SCREAMING_SNAKE_CASE					)

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['token_type_ids']					) ,					sum(tokens_p['token_type_ids']					)					)

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					) ,					sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					) ,					)

                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
                    SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['input_ids'] ,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
                    self.assertSequenceEqual(tokens_r['input_ids'] ,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

                    self.assertSequenceEqual(
                        _SCREAMING_SNAKE_CASE ,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
                    self.assertSequenceEqual(
                        _SCREAMING_SNAKE_CASE ,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for trim_offsets, add_prefix_space in itertools.product([True, False] ,					repeat=2					):
              SCREAMING_SNAKE_CASE_				:  List[str]           =     self.rust_tokenizer_class.from_pretrained(
                  self.tmpdirname ,					use_fast=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					trim_offsets=_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  str           =     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
              SCREAMING_SNAKE_CASE_				:  str           =     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

              self.assertEqual(pre_tokenizer_state['add_prefix_space'] ,					_SCREAMING_SNAKE_CASE					)

              self.assertEqual(post_processor_state['add_prefix_space'] ,					_SCREAMING_SNAKE_CASE					)
              self.assertEqual(post_processor_state['trim_offsets'] ,					_SCREAMING_SNAKE_CASE					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                    SCREAMING_SNAKE_CASE_				:  int           =     f"{text_of_1_token} {text_of_1_token}"

                    SCREAMING_SNAKE_CASE_				:  Tuple           =     self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE ,					use_fast=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					trim_offsets=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer_r(_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
                    self.assertEqual(encoding.offset_mapping[0] ,					(0, len(_SCREAMING_SNAKE_CASE					))					)
                    self.assertEqual(
                        encoding.offset_mapping[1] ,					(len(_SCREAMING_SNAKE_CASE					) + 1, len(_SCREAMING_SNAKE_CASE					) + 1 + len(_SCREAMING_SNAKE_CASE					)) ,					)

                    SCREAMING_SNAKE_CASE_				:  int           =     self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE ,					use_fast=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					trim_offsets=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  int           =     tokenizer_r(_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
                    self.assertEqual(encoding.offset_mapping[0] ,					(0, len(_SCREAMING_SNAKE_CASE					))					)
                    self.assertEqual(
                        encoding.offset_mapping[1] ,					(len(_SCREAMING_SNAKE_CASE					) + 1, len(_SCREAMING_SNAKE_CASE					) + 1 + len(_SCREAMING_SNAKE_CASE					)) ,					)

                    SCREAMING_SNAKE_CASE_				:  str           =     self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE ,					use_fast=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					trim_offsets=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  int           =     tokenizer_r(_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
                    self.assertEqual(encoding.offset_mapping[0] ,					(0, len(_SCREAMING_SNAKE_CASE					))					)
                    self.assertEqual(
                        encoding.offset_mapping[1] ,					(len(_SCREAMING_SNAKE_CASE					), len(_SCREAMING_SNAKE_CASE					) + 1 + len(_SCREAMING_SNAKE_CASE					)) ,					)

                    SCREAMING_SNAKE_CASE_				:  Any           =     self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE ,					use_fast=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					trim_offsets=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer_r(_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
                    self.assertEqual(encoding.offset_mapping[0] ,					(0, len(_SCREAMING_SNAKE_CASE					))					)
                    self.assertEqual(
                        encoding.offset_mapping[1] ,					(len(_SCREAMING_SNAKE_CASE					), len(_SCREAMING_SNAKE_CASE					) + 1 + len(_SCREAMING_SNAKE_CASE					)) ,					)

                    SCREAMING_SNAKE_CASE_				:  Dict           =     f" {text}"

                    # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                    # )
                    # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                    # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                    # self.assertEqual(
                    #     encoding.offset_mapping[1],
                    #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                    # )

                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE ,					use_fast=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					trim_offsets=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer_r(_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
                    self.assertEqual(encoding.offset_mapping[0] ,					(1, 1 + len(_SCREAMING_SNAKE_CASE					))					)
                    self.assertEqual(
                        encoding.offset_mapping[1] ,					(1 + len(_SCREAMING_SNAKE_CASE					) + 1, 1 + len(_SCREAMING_SNAKE_CASE					) + 1 + len(_SCREAMING_SNAKE_CASE					)) ,					)

                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE ,					use_fast=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					trim_offsets=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer_r(_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
                    self.assertEqual(encoding.offset_mapping[0] ,					(0, 1 + len(_SCREAMING_SNAKE_CASE					))					)
                    self.assertEqual(
                        encoding.offset_mapping[1] ,					(1 + len(_SCREAMING_SNAKE_CASE					), 1 + len(_SCREAMING_SNAKE_CASE					) + 1 + len(_SCREAMING_SNAKE_CASE					)) ,					)

                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.rust_tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE ,					use_fast=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					trim_offsets=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer_r(_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
                    self.assertEqual(encoding.offset_mapping[0] ,					(0, 1 + len(_SCREAMING_SNAKE_CASE					))					)
                    self.assertEqual(
                        encoding.offset_mapping[1] ,					(1 + len(_SCREAMING_SNAKE_CASE					), 1 + len(_SCREAMING_SNAKE_CASE					) + 1 + len(_SCREAMING_SNAKE_CASE					)) ,					)









import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  (PNDMScheduler,)
  SCREAMING_SNAKE_CASE    :      Dict       =  (('''num_inference_steps''', 50),)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {
            'num_train_timesteps': 1000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**_SCREAMING_SNAKE_CASE					)
        return config

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Any           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     dummy_past_residuals[:]

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

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Any           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  int           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # copy over dummy past residuals (must be after setting timesteps)
              SCREAMING_SNAKE_CASE_				:  Tuple           =     dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Dict           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

                    # copy over dummy past residual (must be after setting timesteps)
                    SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  List[str]           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     10
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        for i, t in enumerate(scheduler.prk_timesteps					):
              SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps					):
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        return sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  int           =     0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              SCREAMING_SNAKE_CASE_				:  Dict           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.get_scheduler_config(steps_offset=1					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
        scheduler.set_timesteps(10					)
        assert torch.equal(
            scheduler.timesteps ,					torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					) ,					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for beta_start, beta_end in zip([0.0001, 0.001] ,					[0.002, 0.02]					):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE ,					beta_end=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t in [1, 5, 10]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t, num_inference_steps in zip([1, 5, 10] ,					[10, 50, 100]					):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     27

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  int           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample

              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                    SCREAMING_SNAKE_CASE_				:  int           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  str           =     self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.step_plms(self.dummy_sample ,					1 ,					self.dummy_sample					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.full_loop()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 198.1318					) < 1e-2
        assert abs(result_mean.item() - 0.2580					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.full_loop(prediction_type='v_prediction'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 67.3986					) < 1e-2
        assert abs(result_mean.item() - 0.0878					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 230.0399					) < 1e-2
        assert abs(result_mean.item() - 0.2995					) < 1e-3



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 186.9482					) < 1e-2
        assert abs(result_mean.item() - 0.2434					) < 1e-3






