






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					str					     =    (DDIMScheduler,)
		UpperCAmelCase__   :					Dict					     =    (("""eta""", 0.0), ("""num_inference_steps""", 50))


		def 	__snake_case      (    self			,     **UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= {
						    'num_train_timesteps': 1000,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'clip_sample': True,
						}

						config.update(**UpperCamelCase__							)
						return config


		def 	__snake_case      (    self			,     **UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_ :						int    				= self.get_scheduler_config(**UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= scheduler_class(**UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						str    				= 10, 0.0

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.dummy_model()
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.dummy_sample_deter

						scheduler.set_timesteps(UpperCamelCase__							)

						for t in scheduler.timesteps:
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= model(UpperCamelCase__			,     UpperCamelCase__							)
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= scheduler.step(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							).prev_sample

						return sample


		def 	__snake_case      (    self							):
						for timesteps in [100, 500, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCamelCase__							)


		def 	__snake_case      (    self							):
						for steps_offset in [0, 1]:
										self.check_over_configs(steps_offset=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						int    				= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.get_scheduler_config(steps_offset=1							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= scheduler_class(**UpperCamelCase__							)
						scheduler.set_timesteps(5							)
						assert torch.equal(scheduler.timesteps			,     torch.LongTensor([801, 601, 401, 201, 1]							)							)


		def 	__snake_case      (    self							):
						for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,     [0.0_02, 0.02, 0.2, 2]							):
										self.check_over_configs(beta_start=UpperCamelCase__			,     beta_end=UpperCamelCase__							)


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


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


		def 	__snake_case      (    self							):
						for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=UpperCamelCase__							)


		def 	__snake_case      (    self							):
						for timestep_spacing in ["trailing", "leading"]:
										self.check_over_configs(timestep_spacing=UpperCamelCase__							)


		def 	__snake_case      (    self							):
						for rescale_betas_zero_snr in [True, False]:
										self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase__							)


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


		def 	__snake_case      (    self							):
						for t in [1, 10, 49]:
										self.check_over_forward(time_step=UpperCamelCase__							)


		def 	__snake_case      (    self							):
						for t, num_inference_steps in zip([1, 10, 50]			,     [10, 50, 500]							):
										self.check_over_forward(time_step=UpperCamelCase__			,     num_inference_steps=UpperCamelCase__							)


		def 	__snake_case      (    self							):
						for t, eta in zip([1, 10, 49]			,     [0.0, 0.5, 1.0]							):
										self.check_over_forward(time_step=UpperCamelCase__			,     eta=UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Any    				= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_ :						int    				= self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= scheduler_class(**UpperCamelCase__							)

						assert torch.sum(torch.abs(scheduler._get_variance(0			,     0							) - 0.0							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420			,     400							) - 0.1_47_71							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980			,     960							) - 0.3_24_60							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0			,     0							) - 0.0							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487			,     486							) - 0.0_09_79							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999			,     998							) - 0.02							)							) < 1E-5


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.full_loop()

						SCREAMING_SNAKE_CASE_ :						List[str]    				= torch.sum(torch.abs(UpperCamelCase__							)							)
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= torch.mean(torch.abs(UpperCamelCase__							)							)

						assert abs(result_sum.item() - 1_72.00_67							) < 1E-2
						assert abs(result_mean.item() - 0.22_39_67							) < 1E-3


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.full_loop(prediction_type='v_prediction'							)

						SCREAMING_SNAKE_CASE_ :						List[str]    				= torch.sum(torch.abs(UpperCamelCase__							)							)
						SCREAMING_SNAKE_CASE_ :						str    				= torch.mean(torch.abs(UpperCamelCase__							)							)

						assert abs(result_sum.item() - 52.53_02							) < 1E-2
						assert abs(result_mean.item() - 0.06_84							) < 1E-3


		def 	__snake_case      (    self							):
						# We specify different beta, so that the first alpha is 0.99
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.full_loop(set_alpha_to_one=UpperCamelCase__			,     beta_start=0.01							)
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= torch.sum(torch.abs(UpperCamelCase__							)							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= torch.mean(torch.abs(UpperCamelCase__							)							)

						assert abs(result_sum.item() - 1_49.82_95							) < 1E-2
						assert abs(result_mean.item() - 0.19_51							) < 1E-3






		def 	__snake_case      (    self							):
						# We specify different beta, so that the first alpha is 0.99
						SCREAMING_SNAKE_CASE_ :						str    				= self.full_loop(set_alpha_to_one=UpperCamelCase__			,     beta_start=0.01							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= torch.sum(torch.abs(UpperCamelCase__							)							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= torch.mean(torch.abs(UpperCamelCase__							)							)

						assert abs(result_sum.item() - 1_49.07_84							) < 1E-2
						assert abs(result_mean.item() - 0.19_41							) < 1E-3







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


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





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    HerbertTokenizer
		UpperCAmelCase__   :					List[Any]					     =    HerbertTokenizerFast
		UpperCAmelCase__   :					Optional[Any]					     =    True


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

						# Use a simpler test file without japanese/chinese characters
						with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''			,     encoding='utf-8'							) as f_data:
										SCREAMING_SNAKE_CASE_ :						Dict    				= f_data.read().replace('\n\n'			,     '\n'							).strip()

						SCREAMING_SNAKE_CASE_ :						str    				= [
						    '<s>',
						    '</s>',
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    'w</w>',
						    'r</w>',
						    't</w>',
						    'lo',
						    'low',
						    'er</w>',
						    'low</w>',
						    'lowest</w>',
						    'newer</w>',
						    'wider</w>',
						    ',</w>',
						    '<unk>',
						]
						SCREAMING_SNAKE_CASE_ :						str    				= dict(zip(UpperCamelCase__			,     range(len(UpperCamelCase__							)							)							)							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['merges_file']							)
						with open(self.vocab_file			,     'w'							) as fp:
										fp.write(json.dumps(UpperCamelCase__							)							)
						with open(self.merges_file			,     'w'							) as fp:
										fp.write('\n'.join(UpperCamelCase__							)							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 'lower newer'
						SCREAMING_SNAKE_CASE_ :						int    				= 'lower newer'
						return input_text, output_text


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.tokenizer_class(vocab_file=self.vocab_file			,     merges_file=self.merges_file							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 'lower'
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ['low', 'er</w>']
						SCREAMING_SNAKE_CASE_ :						int    				= tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= tokens + ['<unk>']
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= [16, 17, 23]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__							)			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						if not self.test_rust_tokenizer:
										return

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE_ :						List[str]    				= 'lower,newer'

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.tokenize(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= rust_tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= rust_tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.encode(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= rust_tokenizer.encode(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)






		@slow
		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'							)

						SCREAMING_SNAKE_CASE_ :						List[str]    				= tokenizer.encode('konstruowanie sekwencji'			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.encode('konstruowanie wielu sekwencji'			,     add_special_tokens=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Any    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__			,     UpperCamelCase__							)

						assert encoded_sentence == [0] + text + [2]
						assert encoded_pair == [0] + text + [2] + text_a + [2]