


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class       a__				( SCREAMING_SNAKE_CASE__      ):







				"""simple docstring"""



				__UpperCamelCase    :		List[Any]	=	(DDPMParallelScheduler,)




				def 					_snake_case  (self       ,			**__lowercase		):
										__lowerCAmelCase							       =				{
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0_0_0_1,
										    """beta_end""": 0.0_2,
										    """beta_schedule""": """linear""",
										    """variance_type""": """fixed_small""",
										    """clip_sample""": True,
										}

										config.update(**_lowercase		)
										return config




				def 					_snake_case  (self		):

										for timesteps in [1, 5, 1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=_lowercase		)




				def 					_snake_case  (self		):

										for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,			[0.0_0_2, 0.0_2, 0.2, 2]		):
																self.check_over_configs(beta_start=_lowercase       ,			beta_end=_lowercase		)




				def 					_snake_case  (self		):

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




				def 					_snake_case  (self		):

										for variance in ["fixed_small", "fixed_large", "other"]:
																self.check_over_configs(variance_type=_lowercase		)




				def 					_snake_case  (self		):

										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=_lowercase		)




				def 					_snake_case  (self		):
										self.check_over_configs(thresholding=_lowercase		)
										for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "sample", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=_lowercase       ,			prediction_type=_lowercase       ,			sample_max_value=_lowercase       ,			)




				def 					_snake_case  (self		):

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




				def 					_snake_case  (self		):

										for t in [0, 5_00, 9_99]:
																self.check_over_forward(time_step=_lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**_lowercase		)

										assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.0_0_9_7_9		)		) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.0_2		)		) < 1e-5




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**_lowercase		)

										__lowerCAmelCase							       =				len(_lowercase		)

										__lowerCAmelCase							       =				self.dummy_model()
										__lowerCAmelCase							       =				self.dummy_sample_deter
										__lowerCAmelCase							       =				self.dummy_sample_deter + 0.1
										__lowerCAmelCase							       =				self.dummy_sample_deter - 0.1

										__lowerCAmelCase							       =				samplea.shape[0]
										__lowerCAmelCase							       =				torch.stack([samplea, samplea, samplea]       ,			dim=0		)
										__lowerCAmelCase							       =				torch.arange(_lowercase		)[0:3, None].repeat(1       ,			_lowercase		)

										__lowerCAmelCase							       =				model(samples.flatten(0       ,			1		)       ,			timesteps.flatten(0       ,			1		)		)
										__lowerCAmelCase							       =				scheduler.batch_step_no_noise(_lowercase       ,			timesteps.flatten(0       ,			1		)       ,			samples.flatten(0       ,			1		)		)

										__lowerCAmelCase							       =				torch.sum(torch.abs(_lowercase		)		)
										__lowerCAmelCase							       =				torch.mean(torch.abs(_lowercase		)		)

										assert abs(result_sum.item() - 1_1_5_3.1_8_3_3		) < 1e-2
										assert abs(result_mean.item() - 0.5_0_0_5		) < 1e-3




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**_lowercase		)

										__lowerCAmelCase							       =				len(_lowercase		)

										__lowerCAmelCase							       =				self.dummy_model()
										__lowerCAmelCase							       =				self.dummy_sample_deter
										__lowerCAmelCase							       =				torch.manual_seed(0		)

										for t in reversed(range(_lowercase		)		):
																# 1. predict noise residual
																__lowerCAmelCase							       =				model(_lowercase       ,			_lowercase		)

																# 2. predict previous mean of sample x_t-1
																__lowerCAmelCase							       =				scheduler.step(_lowercase       ,			_lowercase       ,			_lowercase       ,			generator=_lowercase		).prev_sample

																__lowerCAmelCase							       =				pred_prev_sample

										__lowerCAmelCase							       =				torch.sum(torch.abs(_lowercase		)		)
										__lowerCAmelCase							       =				torch.mean(torch.abs(_lowercase		)		)

										assert abs(result_sum.item() - 2_5_8.9_6_0_6		) < 1e-2
										assert abs(result_mean.item() - 0.3_3_7_2		) < 1e-3




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config(prediction_type='''v_prediction'''		)
										__lowerCAmelCase							       =				scheduler_class(**_lowercase		)

										__lowerCAmelCase							       =				len(_lowercase		)

										__lowerCAmelCase							       =				self.dummy_model()
										__lowerCAmelCase							       =				self.dummy_sample_deter
										__lowerCAmelCase							       =				torch.manual_seed(0		)

										for t in reversed(range(_lowercase		)		):
																# 1. predict noise residual
																__lowerCAmelCase							       =				model(_lowercase       ,			_lowercase		)

																# 2. predict previous mean of sample x_t-1
																__lowerCAmelCase							       =				scheduler.step(_lowercase       ,			_lowercase       ,			_lowercase       ,			generator=_lowercase		).prev_sample

																__lowerCAmelCase							       =				pred_prev_sample

										__lowerCAmelCase							       =				torch.sum(torch.abs(_lowercase		)		)
										__lowerCAmelCase							       =				torch.mean(torch.abs(_lowercase		)		)

										assert abs(result_sum.item() - 2_0_2.0_2_9_6		) < 1e-2
										assert abs(result_mean.item() - 0.2_6_3_1		) < 1e-3




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**_lowercase		)

										__lowerCAmelCase							       =				[1_00, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=_lowercase		)

										__lowerCAmelCase							       =				scheduler.timesteps

										for i, timestep in enumerate(_lowercase		):
																if i == len(_lowercase		) - 1:
																						__lowerCAmelCase							       =				-1
																else:
																						__lowerCAmelCase							       =				timesteps[i + 1]

																__lowerCAmelCase							       =				scheduler.previous_timestep(_lowercase		)
																__lowerCAmelCase							       =				prev_t.item()

																self.assertEqual(_lowercase       ,			_lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**_lowercase		)

										__lowerCAmelCase							       =				[1_00, 87, 50, 51, 0]

										with self.assertRaises(_lowercase       ,			msg='''`custom_timesteps` must be in descending order.'''		):
																scheduler.set_timesteps(timesteps=_lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**_lowercase		)

										__lowerCAmelCase							       =				[1_00, 87, 50, 1, 0]
										__lowerCAmelCase							       =				len(_lowercase		)

										with self.assertRaises(_lowercase       ,			msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
																scheduler.set_timesteps(num_inference_steps=_lowercase       ,			timesteps=_lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**_lowercase		)

										__lowerCAmelCase							       =				[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    _lowercase       ,			msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,			):
																scheduler.set_timesteps(timesteps=_lowercase		)





'''simple docstring'''

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		int	=	MgpstrTokenizer
				__UpperCamelCase    :		Optional[Any]	=	False
				__UpperCamelCase    :		Optional[int]	=	{}
				__UpperCamelCase    :		List[Any]	=	False




				def 					_snake_case  (self		):
										super().setUp()

										# fmt: off
										__lowerCAmelCase							       =				['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
										# fmt: on
										__lowerCAmelCase							       =				dict(zip(__lowercase       ,			range(len(__lowercase		)		)		)		)

										__lowerCAmelCase							       =				os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES['''vocab_file''']		)
										with open(self.vocab_file       ,			'''w'''       ,			encoding='''utf-8'''		) as fp:
																fp.write(json.dumps(__lowercase		) + '''\n'''		)




				def 					_snake_case  (self       ,			**__lowercase		):
										return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				'''tester'''
										__lowerCAmelCase							       =				'''tester'''
										return input_text, output_text




				@unittest.skip('''MGP-STR always lower cases letters.'''		)
				def 					_snake_case  (self		):
										pass




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_tokenizers(do_lower_case=__lowercase		)
										for tokenizer in tokenizers:
																with self.subTest(F"""{tokenizer.__class__.__name__}"""		):
																						__lowerCAmelCase							       =				'''[SPECIAL_TOKEN]'''

																						tokenizer.add_special_tokens({'''cls_token''': special_token}		)
																						__lowerCAmelCase							       =				tokenizer.encode([special_token]       ,			add_special_tokens=__lowercase		)
																						self.assertEqual(len(__lowercase		)       ,			1		)

																						__lowerCAmelCase							       =				tokenizer.decode(__lowercase       ,			skip_special_tokens=__lowercase		)
																						self.assertTrue(special_token not in decoded		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_tokenizers()
										for tokenizer in tokenizers:
																with self.subTest(F"""{tokenizer.__class__.__name__}"""		):
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.get_input_output_texts(__lowercase		)

																						__lowerCAmelCase							       =				tokenizer.tokenize(__lowercase		)
																						__lowerCAmelCase							       =				tokenizer.convert_tokens_to_ids(__lowercase		)
																						__lowerCAmelCase							       =				tokenizer.encode(__lowercase       ,			add_special_tokens=__lowercase		)
																						self.assertListEqual(__lowercase       ,			__lowercase		)

																						__lowerCAmelCase							       =				tokenizer.convert_ids_to_tokens(__lowercase		)
																						self.assertNotEqual(len(__lowercase		)       ,			0		)
																						__lowerCAmelCase							       =				tokenizer.decode(__lowercase		)
																						self.assertIsInstance(__lowercase       ,			__lowercase		)

																						self.assertEqual(text_a.replace(''' '''       ,			''''''		)       ,			__lowercase		)




				@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''		)
				def 					_snake_case  (self		):
										pass




				@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''		)
				def 					_snake_case  (self		):
										pass

