"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Union[str, Any]					=	(UniPCMultistepScheduler,)
				UpperCAmelCase       :   Any					=	(("""num_inference_steps""", 25),)



				def       _lowercase  (self       :   Any			,						**_A       :   Optional[int])					->			int:
					__snake_case    :							int   	=				{
					    'num_train_timesteps': 10_00,
					    'beta_start': 0.0_001,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					    'solver_order': 2,
					    'solver_type': 'bh2',
					}

					config.update(**_A)
					return config



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   Optional[Any]=0			,						**_A       :   Optional[int])					->			List[str]:
					__snake_case    :							Optional[Any]   	=				dict(self.forward_default_kwargs)
					__snake_case    :							int   	=				kwargs.pop('num_inference_steps'			,						_A)
					__snake_case    :							str   	=				self.dummy_sample
					__snake_case    :							int   	=				0.1 * sample
					__snake_case    :							List[str]   	=				[residual + 0.2, residual + 0.15, residual + 0.10]

					for scheduler_class in self.scheduler_classes:
						__snake_case    :							Optional[int]   	=				self.get_scheduler_config(**_A)
						__snake_case    :							Union[str, Any]   	=				scheduler_class(**_A)
						scheduler.set_timesteps(_A)
						# copy over dummy past residuals
						__snake_case    :							Any   	=				dummy_past_residuals[: scheduler.config.solver_order]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(_A)
							__snake_case    :							List[Any]   	=				scheduler_class.from_pretrained(_A)
							new_scheduler.set_timesteps(_A)
							# copy over dummy past residuals
							__snake_case    :							Tuple   	=				dummy_past_residuals[: new_scheduler.config.solver_order]

						__snake_case	,       __snake_case    :							str   	=				sample, sample
						for t in range(_A			,						time_step + scheduler.config.solver_order + 1):
							__snake_case    :							int   	=				scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample
							__snake_case    :							List[str]   	=				new_scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample

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



				def       _lowercase  (self       :   List[Any]			,						_A       :   Any=0			,						**_A       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				dict(self.forward_default_kwargs)
					__snake_case    :							Union[str, Any]   	=				kwargs.pop('num_inference_steps'			,						_A)
					__snake_case    :							List[str]   	=				self.dummy_sample
					__snake_case    :							Optional[int]   	=				0.1 * sample
					__snake_case    :							Optional[Any]   	=				[residual + 0.2, residual + 0.15, residual + 0.10]

					for scheduler_class in self.scheduler_classes:
						__snake_case    :							Union[str, Any]   	=				self.get_scheduler_config()
						__snake_case    :							Optional[Any]   	=				scheduler_class(**_A)
						scheduler.set_timesteps(_A)

						# copy over dummy past residuals (must be after setting timesteps)
						__snake_case    :							Tuple   	=				dummy_past_residuals[: scheduler.config.solver_order]

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

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

						__snake_case    :							Any   	=				scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample
						__snake_case    :							Tuple   	=				new_scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample

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



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   str=None			,						**_A       :   Any)					->			Union[str, Any]:
					if scheduler is None:
						__snake_case    :							Tuple   	=				self.scheduler_classes[0]
						__snake_case    :							Tuple   	=				self.get_scheduler_config(**_A)
						__snake_case    :							str   	=				scheduler_class(**_A)

					__snake_case    :							Dict   	=				self.scheduler_classes[0]
					__snake_case    :							Optional[Any]   	=				self.get_scheduler_config(**_A)
					__snake_case    :							List[str]   	=				scheduler_class(**_A)

					__snake_case    :							Any   	=				10
					__snake_case    :							int   	=				self.dummy_model()
					__snake_case    :							int   	=				self.dummy_sample_deter
					scheduler.set_timesteps(_A)

					for i, t in enumerate(scheduler.timesteps):
						__snake_case    :							Dict   	=				model(_A			,						_A)
						__snake_case    :							Optional[Any]   	=				scheduler.step(_A			,						_A			,						_A).prev_sample

					return sample



				def       _lowercase  (self       :   Optional[Any])					->			Tuple:
					__snake_case    :							Dict   	=				dict(self.forward_default_kwargs)

					__snake_case    :							Any   	=				kwargs.pop('num_inference_steps'			,						_A)

					for scheduler_class in self.scheduler_classes:
						__snake_case    :							Any   	=				self.get_scheduler_config()
						__snake_case    :							str   	=				scheduler_class(**_A)

						__snake_case    :							Union[str, Any]   	=				self.dummy_sample
						__snake_case    :							str   	=				0.1 * sample

						if num_inference_steps is not None and hasattr(_A			,						'set_timesteps'):
							scheduler.set_timesteps(_A)
						elif num_inference_steps is not None and not hasattr(_A			,						'set_timesteps'):
							__snake_case    :							str   	=				num_inference_steps

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

						__snake_case    :							Dict   	=				scheduler.timesteps[5]
						__snake_case    :							Tuple   	=				scheduler.timesteps[6]

						__snake_case    :							str   	=				scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample
						__snake_case    :							Any   	=				scheduler.step(_A			,						_A			,						_A			,						**_A).prev_sample

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



				def       _lowercase  (self       :   Optional[Any])					->			Union[str, Any]:
					# make sure that iterating over schedulers with same config names gives same results
					# for defaults
					__snake_case    :							Optional[Any]   	=				UniPCMultistepScheduler(**self.get_scheduler_config())
					__snake_case    :							int   	=				self.full_loop(scheduler=_A)
					__snake_case    :							Union[str, Any]   	=				torch.mean(torch.abs(_A))

					assert abs(result_mean.item() - 0.2_464) < 1E-3

					__snake_case    :							Union[str, Any]   	=				DPMSolverSinglestepScheduler.from_config(scheduler.config)
					__snake_case    :							List[Any]   	=				DEISMultistepScheduler.from_config(scheduler.config)
					__snake_case    :							str   	=				DPMSolverMultistepScheduler.from_config(scheduler.config)
					__snake_case    :							Optional[int]   	=				UniPCMultistepScheduler.from_config(scheduler.config)

					__snake_case    :							Optional[int]   	=				self.full_loop(scheduler=_A)
					__snake_case    :							Optional[Any]   	=				torch.mean(torch.abs(_A))

					assert abs(result_mean.item() - 0.2_464) < 1E-3



				def       _lowercase  (self       :   Optional[Any])					->			Dict:
					for timesteps in [25, 50, 1_00, 9_99, 10_00]:
						self.check_over_configs(num_train_timesteps=_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					self.check_over_configs(thresholding=_A)
					for order in [1, 2, 3]:
						for solver_type in ["bh1", "bh2"]:
							for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(
									    thresholding=_A			,						prediction_type=_A			,						sample_max_value=_A			,						solver_order=_A			,						solver_type=_A			,						)



				def       _lowercase  (self       :   Optional[Any])					->			str:
					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=_A)



				def       _lowercase  (self       :   List[str])					->			int:
					for solver_type in ["bh1", "bh2"]:
						for order in [1, 2, 3]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    solver_order=_A			,						solver_type=_A			,						prediction_type=_A			,						)
								__snake_case    :							Optional[int]   	=				self.full_loop(
								    solver_order=_A			,						solver_type=_A			,						prediction_type=_A			,						)
								assert not torch.isnan(_A).any(), "Samples have nan numbers"



				def       _lowercase  (self       :   str)					->			Union[str, Any]:
					self.check_over_configs(lower_order_final=_A)
					self.check_over_configs(lower_order_final=_A)



				def       _lowercase  (self       :   Optional[Any])					->			List[Any]:
					for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
						self.check_over_forward(num_inference_steps=_A			,						time_step=0)



				def       _lowercase  (self       :   Tuple)					->			Any:
					__snake_case    :							List[Any]   	=				self.full_loop()
					__snake_case    :							Union[str, Any]   	=				torch.mean(torch.abs(_A))

					assert abs(result_mean.item() - 0.2_464) < 1E-3



				def       _lowercase  (self       :   str)					->			Optional[Any]:
					__snake_case    :							Optional[Any]   	=				self.full_loop(prediction_type='v_prediction')
					__snake_case    :							int   	=				torch.mean(torch.abs(_A))

					assert abs(result_mean.item() - 0.1_014) < 1E-3



				def       _lowercase  (self       :   str)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				self.scheduler_classes[0]
					__snake_case    :							List[str]   	=				self.get_scheduler_config(thresholding=_A			,						dynamic_thresholding_ratio=0)
					__snake_case    :							List[Any]   	=				scheduler_class(**_A)

					__snake_case    :							int   	=				10
					__snake_case    :							str   	=				self.dummy_model()
					__snake_case    :							str   	=				self.dummy_sample_deter.half()
					scheduler.set_timesteps(_A)

					for i, t in enumerate(scheduler.timesteps):
						__snake_case    :							List[str]   	=				model(_A			,						_A)
						__snake_case    :							int   	=				scheduler.step(_A			,						_A			,						_A).prev_sample

					assert sample.dtype == torch.floataa






				def       _lowercase  (self       :   List[Any]			,						**_A       :   Union[str, Any])					->			Dict:
					for scheduler_class in self.scheduler_classes:
						__snake_case    :							str   	=				self.get_scheduler_config(**_A)
						__snake_case    :							Optional[Any]   	=				scheduler_class(**_A)

						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 BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[Any]					=	MvpTokenizer
				UpperCAmelCase       :   int					=	MvpTokenizerFast
				UpperCAmelCase       :   Optional[Any]					=	True
				UpperCAmelCase       :   Tuple					=	filter_roberta_detectors



				def       _lowercase  (self       :   Any)					->			str:
					super().setUp()
					__snake_case    :							Optional[Any]   	=				[
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    '\u0120',
					    '\u0120l',
					    '\u0120n',
					    '\u0120lo',
					    '\u0120low',
					    'er',
					    '\u0120lowest',
					    '\u0120newer',
					    '\u0120wider',
					    '<unk>',
					]
					__snake_case    :							List[Any]   	=				dict(zip(_A			,						range(len(_A))))
					__snake_case    :							Optional[Any]   	=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
					__snake_case    :							Optional[Any]   	=				{'unk_token': '<unk>'}

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



				def       _lowercase  (self       :   List[str]			,						**_A       :   Any)					->			str:
					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Optional[Any]			,						**_A       :   Tuple)					->			Any:
					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			Optional[Any]:
					return "lower newer", "lower newer"



				@cached_property
				def       _lowercase  (self       :   Tuple)					->			int:
					return MvpTokenizer.from_pretrained('RUCAIBox/mvp')



				@cached_property
				def       _lowercase  (self       :   int)					->			Tuple:
					return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					__snake_case    :							Optional[int]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					__snake_case    :							Any   	=				[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							str   	=				tokenizer(_A			,						max_length=len(_A)			,						padding=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)

						self.assertEqual((2, 9)			,						batch.input_ids.shape)
						self.assertEqual((2, 9)			,						batch.attention_mask.shape)
						__snake_case    :							List[str]   	=				batch.input_ids.tolist()[0]
						self.assertListEqual(_A			,						_A)
						# Test that special tokens are reset



				@require_torch
				def       _lowercase  (self       :   List[str])					->			Any:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Tuple   	=				tokenizer(_A			,						padding=_A			,						return_tensors='pt')
						# check if input_ids are returned and no labels
						self.assertIn('input_ids'			,						_A)
						self.assertIn('attention_mask'			,						_A)
						self.assertNotIn('labels'			,						_A)
						self.assertNotIn('decoder_attention_mask'			,						_A)



				@require_torch
				def       _lowercase  (self       :   List[Any])					->			Tuple:
					__snake_case    :							List[Any]   	=				[
					    'Summary of the text.',
					    'Another summary.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(text_target=_A			,						max_length=32			,						padding='max_length'			,						return_tensors='pt')
						self.assertEqual(32			,						targets['input_ids'].shape[1])



				@require_torch
				def       _lowercase  (self       :   Tuple)					->			Dict:
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Dict   	=				tokenizer(
						    ['I am a small frog' * 10_24, 'I am a small frog']			,						padding=_A			,						truncation=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)
						self.assertEqual(batch.input_ids.shape			,						(2, 10_24))



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			Dict:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.']
					__snake_case    :							str   	=				[
					    'Summary of the text.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(_A			,						text_target=_A			,						return_tensors='pt')
						__snake_case    :							str   	=				inputs['input_ids']
						__snake_case    :							Optional[Any]   	=				inputs['labels']
						self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
						self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



				def       _lowercase  (self       :   str)					->			Tuple:
					pass






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

							# 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'])			,						)

							__snake_case    :							Tuple   	=				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
							__snake_case    :							Union[str, 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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
							self.assertSequenceEqual(tokens_r['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

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





