"""simple docstring"""


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
 from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
 lowerCAmelCase_:					Union[str, Any]     =					get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
 from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCAmelCase_:					Any     =					1_2_8_0_2_2
lowerCAmelCase_:					List[Any]     =					1_2_8_0_2_8






@require_sentencepiece
class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      MaMaaaTokenizer
   snake_case_							=      False
   snake_case_							=      False
   snake_case_							=      True


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().setUp()

        lowercase__				 =							["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
        lowercase__				 =							dict(zip(_UpperCAmelCase,				range(len(_UpperCAmelCase  )  )  )  )
        lowercase__				 =							Path(self.tmpdirname  )
        save_json(_UpperCAmelCase,				save_dir / VOCAB_FILES_NAMES["vocab_file"]  )
        if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
             copyfile(_UpperCAmelCase,				save_dir / VOCAB_FILES_NAMES["spm_file"]  )

        lowercase__				 =							MaMaaaTokenizer.from_pretrained(self.tmpdirname  )
        tokenizer.save_pretrained(self.tmpdirname  )


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        return MaMaaaTokenizer.from_pretrained(self.tmpdirname,				**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return (
            "This is a test",
            "This is a test",
        )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"</s>"
        lowercase__				 =							0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase  ),				_UpperCAmelCase  )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase  ),				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.get_tokenizer()
        lowercase__				 =							list(tokenizer.get_vocab().keys()  )

        self.assertEqual(vocab_keys[0],				"</s>"  )
        self.assertEqual(vocab_keys[1],				"<unk>"  )
        self.assertEqual(vocab_keys[-1],				"<s>"  )
        self.assertEqual(len(_UpperCAmelCase  ),				tokenizer.vocab_size + len(tokenizer.get_added_vocab()  )  )


   @unittest.skip("Skip this test while all models are still to be uploaded."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.get_tokenizer()

        lowercase__				 =							tokenizer.tokenize("This is a test"  )
        self.assertListEqual(_UpperCAmelCase,				["▁This", "▁is", "▁a", "▁t", "est"]  )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(_UpperCAmelCase  ),				[2, 3, 4, 5, 6],				)

        lowercase__				 =							tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]  )
        self.assertListEqual(_UpperCAmelCase,				["▁This", "▁is", "▁a", "▁t", "est"]  )

        lowercase__				 =							tokenizer.convert_tokens_to_string(_UpperCAmelCase  )
        self.assertEqual(_UpperCAmelCase,				"This is a test"  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							{"input_ids": [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=_UpperCAmelCase,				model_name="facebook/m2m100_418M",				revision="c168bae485c864188cf9aa0e4108b0b6934dc91e",				)






@require_torch
@require_sentencepiece
@require_tokenizers
class    a__    (   unittest.TestCase	):
   snake_case_							=      "facebook/m2m100_418M"
   snake_case_							=      [
       "In my opinion, there are two levels of response from the French government.",
       "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
   ]
   snake_case_							=      [
       "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
       "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
   ]

   # fmt: off
   snake_case_							=      [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]


   @classmethod
   def 	snake_case__	(     cls  ):
        '''simple docstring'''



        lowercase__				 =							MaMaaaTokenizer.from_pretrained(
            cls.checkpoint_name,				src_lang="en",				tgt_lang="fr"  )
        lowercase__				 =							1
        return cls


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        self.assertEqual(self.tokenizer.get_lang_id("ar"  ),				12_8006  )
        self.assertEqual(self.tokenizer.get_lang_id("en"  ),				12_8022  )
        self.assertEqual(self.tokenizer.get_lang_id("ro"  ),				12_8076  )
        self.assertEqual(self.tokenizer.get_lang_id("mr"  ),				12_8063  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.tokenizer.get_vocab()
        self.assertEqual(len(_UpperCAmelCase  ),				self.tokenizer.vocab_size  )
        self.assertEqual(vocab["<unk>"],				3  )
        self.assertIn(self.tokenizer.get_lang_token("en"  ),				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"en"
        lowercase__				 =							self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
        self.assertListEqual(self.expected_src_tokens,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        self.assertIn(_UpperCAmelCase,				self.tokenizer.all_special_ids  )
        # fmt: off
        lowercase__				 =							[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
        # fmt: on
        lowercase__				 =							self.tokenizer.decode(_UpperCAmelCase,				skip_special_tokens=_UpperCAmelCase  )
        lowercase__				 =							self.tokenizer.decode(generated_ids[1:],				skip_special_tokens=_UpperCAmelCase  )
        self.assertEqual(_UpperCAmelCase,				_UpperCAmelCase  )
        self.assertNotIn(self.tokenizer.eos_token,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							tempfile.mkdtemp()
        lowercase__				 =							self.tokenizer.lang_token_to_id
        self.tokenizer.save_pretrained(_UpperCAmelCase  )
        lowercase__				 =							MaMaaaTokenizer.from_pretrained(_UpperCAmelCase  )
        self.assertDictEqual(new_tok.lang_token_to_id,				_UpperCAmelCase  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"en"
        lowercase__				 =							"fr"

        lowercase__				 =							self.tokenizer(self.src_text,				text_target=self.tgt_text,				padding=_UpperCAmelCase,				return_tensors="pt"  )

        lowercase__				 =							shift_tokens_right(
            batch["labels"],				self.tokenizer.pad_token_id,				self.tokenizer.eos_token_id  )

        for k in batch:
             lowercase__				 =							batch[k].tolist()
        # batch = {k: v.tolist() for k,v in batch.items()}
        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        # batch.decoder_inputs_ids[0][0] ==
        assert batch.input_ids[1][0] == EN_CODE
        assert batch.input_ids[1][-1] == 2
        assert batch.labels[1][0] == FR_CODE
        assert batch.labels[1][-1] == 2
        assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"mr"
        self.assertListEqual(self.tokenizer.prefix_tokens,				[self.tokenizer.get_lang_id("mr"  )]  )
        self.assertListEqual(self.tokenizer.suffix_tokens,				[self.tokenizer.eos_token_id]  )

        lowercase__				 =							"zh"
        self.assertListEqual(self.tokenizer.prefix_tokens,				[self.tokenizer.get_lang_id("zh"  )]  )
        self.assertListEqual(self.tokenizer.suffix_tokens,				[self.tokenizer.eos_token_id]  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"mr"
        self.tokenizer._switch_to_target_mode()
        self.assertListEqual(self.tokenizer.prefix_tokens,				[self.tokenizer.get_lang_id("mr"  )]  )
        self.assertListEqual(self.tokenizer.suffix_tokens,				[self.tokenizer.eos_token_id]  )
        self.tokenizer._switch_to_input_mode()
        self.assertListEqual(self.tokenizer.prefix_tokens,				[self.tokenizer.get_lang_id(self.tokenizer.src_lang  )]  )

        lowercase__				 =							"zh"
        self.tokenizer._switch_to_target_mode()
        self.assertListEqual(self.tokenizer.prefix_tokens,				[self.tokenizer.get_lang_id("zh"  )]  )
        self.assertListEqual(self.tokenizer.suffix_tokens,				[self.tokenizer.eos_token_id]  )
        self.tokenizer._switch_to_input_mode()
        self.assertListEqual(self.tokenizer.prefix_tokens,				[self.tokenizer.get_lang_id(self.tokenizer.src_lang  )]  )


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.tokenizer._build_translation_inputs("A test",				return_tensors="pt",				src_lang="en",				tgt_lang="ar"  )

        self.assertEqual(
            nested_simplify(_UpperCAmelCase  ),				{
                # en_XX, A, test, EOS
                "input_ids": [[12_8022, 58, 4183, 2]],
                "attention_mask": [[1, 1, 1, 1]],
                # ar_AR
                "forced_bos_token_id": 12_8006,
            },				)





"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    a__    (   _a	):
   snake_case_							=      (IPNDMScheduler,)
   snake_case_							=      (("num_inference_steps", 50),)


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							{"num_train_timesteps": 1000}
        config.update(**_UpperCAmelCase  )
        return config


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )
        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )
        lowercase__				 =							self.dummy_sample
        lowercase__				 =							0.1 * sample
        lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )
        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )
        lowercase__				 =							self.dummy_sample
        lowercase__				 =							0.1 * sample
        lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



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

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

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

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

        return sample


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )

        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config()
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

             lowercase__				 =							self.dummy_sample
             lowercase__				 =							0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase,				"set_timesteps"  ):
                  scheduler.set_timesteps(_UpperCAmelCase  )
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase,				"set_timesteps"  ):
                  lowercase__				 =							num_inference_steps

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

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop()
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_mean.item() - 254_0529  ) < 10




