




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     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			HerbertTokenizer
					_lowerCamelCase		  =			HerbertTokenizerFast
					_lowerCamelCase		  =			True

					def      lowercase_							(    self ):







						'''simple docstring'''




						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:
							lowerCamelCase_							:		Optional[int]							       =							f_data.read().replace('\n\n'			,	'\n' ).strip()

						lowerCamelCase_							:		Union[str, Any]							       =							[
						    '<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>',
						]
						lowerCamelCase_							:		Optional[int]							       =							dict(zip(__lowercase			,	range(len(__lowercase ) ) ) )
						lowerCamelCase_							:		List[str]							       =							['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						lowerCamelCase_							:		List[str]							       =							os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES['vocab_file'] )
						lowerCamelCase_							:		List[str]							       =							os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES['merges_file'] )
						with open(self.vocab_file			,	'w' ) as fp:
							fp.write(json.dumps(__lowercase ) )
						with open(self.merges_file			,	'w' ) as fp:
							fp.write('\n'.join(__lowercase ) )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.tokenizer_class(vocab_file=self.vocab_file			,	merges_file=self.merges_file )

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

						lowerCamelCase_							:		Optional[int]							       =							tokens + ['<unk>']
						lowerCamelCase_							:		List[str]							       =							[16, 17, 23]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase )			,	__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						if not self.test_rust_tokenizer:
							return

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

						lowerCamelCase_							:		Optional[int]							       =							'lower,newer'

						lowerCamelCase_							:		Tuple							       =							tokenizer.tokenize(__lowercase )
						lowerCamelCase_							:		Any							       =							rust_tokenizer.tokenize(__lowercase )
						self.assertListEqual(__lowercase			,	__lowercase )

						lowerCamelCase_							:		Union[str, Any]							       =							tokenizer.encode(__lowercase			,	add_special_tokens=__lowercase )
						lowerCamelCase_							:		List[Any]							       =							rust_tokenizer.encode(__lowercase			,	add_special_tokens=__lowercase )
						self.assertListEqual(__lowercase			,	__lowercase )

						lowerCamelCase_							:		List[Any]							       =							self.get_rust_tokenizer()
						lowerCamelCase_							:		Union[str, Any]							       =							tokenizer.encode(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							rust_tokenizer.encode(__lowercase )
						self.assertListEqual(__lowercase			,	__lowercase )





					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

						lowerCamelCase_							:		Dict							       =							tokenizer.encode('konstruowanie sekwencji'			,	add_special_tokens=__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							tokenizer.encode('konstruowanie wielu sekwencji'			,	add_special_tokens=__lowercase )

						lowerCamelCase_							:		str							       =							tokenizer.build_inputs_with_special_tokens(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							tokenizer.build_inputs_with_special_tokens(__lowercase			,	__lowercase )

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






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			(HeunDiscreteScheduler,)
					_lowerCamelCase		  =			10

					def      lowercase_							(    self			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							{
						    'num_train_timesteps': 1_100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__lowercase )
						return config

					def      lowercase_							(    self ):







						'''simple docstring'''




						for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,	[0.00_02, 0.0_02, 0.02] ):
							self.check_over_configs(beta_start=__lowercase			,	beta_end=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for schedule in ["linear", "scaled_linear", "exp"]:
							self.check_over_configs(beta_schedule=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for clip_sample_range in [1.0, 2.0, 3.0]:
							self.check_over_configs(clip_sample_range=__lowercase			,	clip_sample=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		List[str]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Union[str, Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		Dict							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		Any							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Optional[int]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Tuple							       =							output.prev_sample

						lowerCamelCase_							:		str							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Tuple							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		str							       =							self.get_scheduler_config(prediction_type='v_prediction' )
						lowerCamelCase_							:		Optional[Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Any							       =							self.dummy_model()
						lowerCamelCase_							:		str							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		List[Any]							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		int							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Any							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		List[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
							assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 4.693428650170972E-07 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		int							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps			,	device=__lowercase )

						lowerCamelCase_							:		str							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
							lowerCamelCase_							:		Tuple							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		List[Any]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Union[str, Any]							       =							output.prev_sample

						lowerCamelCase_							:		Dict							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Any							       =							torch.mean(torch.abs(__lowercase ) )

						if str(__lowercase ).startswith('cpu' ):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						elif str(__lowercase ).startswith('mps' ):
							# Larger tolerance on mps
							assert abs(result_mean.item() - 0.00_02 ) < 1E-2
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		Optional[Any]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase			,	use_karras_sigmas=__lowercase )

						scheduler.set_timesteps(self.num_inference_steps			,	device=__lowercase )

						lowerCamelCase_							:		int							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma
						lowerCamelCase_							:		Tuple							       =							sample.to(__lowercase )

						for t in scheduler.timesteps:
							lowerCamelCase_							:		str							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Dict							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Tuple							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Optional[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						assert abs(result_sum.item() - 0.0_00_15 ) < 1E-2
						assert abs(result_mean.item() - 1.9869554535034695E-07 ) < 1E-2
