
'''simple docstring'''







import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase   ):
	lowercase__	    =							(HeunDiscreteScheduler,)
	lowercase__	    =							10
	def __lowerCamelCase    (				self  ,    **__UpperCamelCase			):



					'''simple docstring'''






					__a       :     List[str]								=	{
					    """num_train_timesteps""": 1100,
					    """beta_start""": 0.0_0_0_1,
					    """beta_end""": 0.0_2,
					    """beta_schedule""": """linear""",
					}

					config.update(**__UpperCamelCase			)
					return config
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__UpperCamelCase			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,    [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
									self.check_over_configs(beta_start=__UpperCamelCase  ,    beta_end=__UpperCamelCase			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






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



					'''simple docstring'''






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



					'''simple docstring'''






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



					'''simple docstring'''






					__a       :     int								=	self.scheduler_classes[0]
					__a       :     int								=	self.get_scheduler_config()
					__a       :     Tuple								=	scheduler_class(**__UpperCamelCase			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__a       :     Dict								=	self.dummy_model()
					__a       :     List[str]								=	self.dummy_sample_deter * scheduler.init_noise_sigma
					__a       :     str								=	sample.to(__UpperCamelCase			)

					for i, t in enumerate(scheduler.timesteps			):
									__a       :     Union[str, Any]								=	scheduler.scale_model_input(__UpperCamelCase  ,    __UpperCamelCase			)

									__a       :     List[str]								=	model(__UpperCamelCase  ,    __UpperCamelCase			)

									__a       :     List[Any]								=	scheduler.step(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
									__a       :     int								=	output.prev_sample

					__a       :     str								=	torch.sum(torch.abs(__UpperCamelCase			)			)
					__a       :     Optional[Any]								=	torch.mean(torch.abs(__UpperCamelCase			)			)

					if torch_device in ["cpu", "mps"]:
									assert abs(result_sum.item() - 0.1_2_3_3			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3
					else:
									# CUDA
									assert abs(result_sum.item() - 0.1_2_3_3			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[str]								=	self.scheduler_classes[0]
					__a       :     List[str]								=	self.get_scheduler_config(prediction_type="""v_prediction"""			)
					__a       :     Any								=	scheduler_class(**__UpperCamelCase			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__a       :     Dict								=	self.dummy_model()
					__a       :     Dict								=	self.dummy_sample_deter * scheduler.init_noise_sigma
					__a       :     Any								=	sample.to(__UpperCamelCase			)

					for i, t in enumerate(scheduler.timesteps			):
									__a       :     Dict								=	scheduler.scale_model_input(__UpperCamelCase  ,    __UpperCamelCase			)

									__a       :     Any								=	model(__UpperCamelCase  ,    __UpperCamelCase			)

									__a       :     Optional[int]								=	scheduler.step(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
									__a       :     str								=	output.prev_sample

					__a       :     Any								=	torch.sum(torch.abs(__UpperCamelCase			)			)
					__a       :     Optional[int]								=	torch.mean(torch.abs(__UpperCamelCase			)			)

					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() - 4.693_4286_5017_0972E-07			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[Any]								=	self.scheduler_classes[0]
					__a       :     List[str]								=	self.get_scheduler_config()
					__a       :     Dict								=	scheduler_class(**__UpperCamelCase			)

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

					__a       :     Optional[int]								=	self.dummy_model()
					__a       :     Optional[Any]								=	self.dummy_sample_deter.to(__UpperCamelCase			) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
									__a       :     Union[str, Any]								=	scheduler.scale_model_input(__UpperCamelCase  ,    __UpperCamelCase			)

									__a       :     str								=	model(__UpperCamelCase  ,    __UpperCamelCase			)

									__a       :     Tuple								=	scheduler.step(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
									__a       :     List[str]								=	output.prev_sample

					__a       :     List[Any]								=	torch.sum(torch.abs(__UpperCamelCase			)			)
					__a       :     Any								=	torch.mean(torch.abs(__UpperCamelCase			)			)

					if str(__UpperCamelCase			).startswith("""cpu"""			):
									# The following sum varies between 148 and 156 on mps. Why?
									assert abs(result_sum.item() - 0.1_2_3_3			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3
					elif str(__UpperCamelCase			).startswith("""mps"""			):
									# Larger tolerance on mps
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-2
					else:
									# CUDA
									assert abs(result_sum.item() - 0.1_2_3_3			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Optional[int]								=	self.scheduler_classes[0]
					__a       :     int								=	self.get_scheduler_config()
					__a       :     List[Any]								=	scheduler_class(**__UpperCamelCase  ,    use_karras_sigmas=__UpperCamelCase			)

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

					__a       :     Union[str, Any]								=	self.dummy_model()
					__a       :     int								=	self.dummy_sample_deter.to(__UpperCamelCase			) * scheduler.init_noise_sigma
					__a       :     List[Any]								=	sample.to(__UpperCamelCase			)

					for t in scheduler.timesteps:
									__a       :     List[str]								=	scheduler.scale_model_input(__UpperCamelCase  ,    __UpperCamelCase			)

									__a       :     str								=	model(__UpperCamelCase  ,    __UpperCamelCase			)

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

					__a       :     Optional[Any]								=	torch.sum(torch.abs(__UpperCamelCase			)			)
					__a       :     Tuple								=	torch.mean(torch.abs(__UpperCamelCase			)			)

					assert abs(result_sum.item() - 0.0_0_0_1_5			) < 1E-2
					assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07			) < 1E-2

'''simple docstring'''







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase			,					unittest.TestCase   ):
	lowercase__	    =							GPTNeoXJapaneseTokenizer
	lowercase__	    =							False
	lowercase__	    =							{"do_clean_text": False, "add_prefix_space": False}
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					super().setUp()

					__a       :     Optional[int]								=	[
					    """こん""",
					    """こんに""",
					    """にちは""",
					    """ばんは""",
					    """世界,㔺界""",
					    """、""",
					    """。""",
					    """<BR>""",
					    """<SP>""",
					    """<TAB>""",
					    """<URL>""",
					    """<EMAIL>""",
					    """<TEL>""",
					    """<DATE>""",
					    """<PRICE>""",
					    """<BLOCK>""",
					    """<KIGOU>""",
					    """<U2000U2BFF>""",
					    """<|emoji1|>""",
					    """<unk>""",
					    """<|startoftext|>""",
					    """<|endoftext|>""",
					]
					__a       :     Any								=	{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
					__a       :     List[str]								=	{"""unk_token""": """<unk>"""}

					__a       :     Any								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""vocab_file"""]			)
					__a       :     Tuple								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""emoji_file"""]			)
					with open(self.vocab_file  ,    """w"""  ,    encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
					with open(self.emoji_file  ,    """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__UpperCamelCase			)			)
	def __lowerCamelCase    (				self  ,    **__UpperCamelCase			):



					'''simple docstring'''






					kwargs.update(self.special_tokens_map			)
					return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname  ,    **__UpperCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Any								=	"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
					__a       :     Optional[int]								=	"""こんにちは、世界。 \nこんばんは、世界。😀"""
					return input_text, output_text
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a							,	__a       :     List[str]								=	self.get_input_output_texts(__UpperCamelCase			)
					__a       :     List[str]								=	tokenizer.encode(__UpperCamelCase  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     str								=	tokenizer.decode(__UpperCamelCase  ,    clean_up_tokenization_spaces=__UpperCamelCase			)
					return text, ids
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[Any]								=	self.get_tokenizer()

					# Testing tokenization
					__a       :     Dict								=	"""こんにちは、世界。　こんばんは、㔺界。"""
					__a       :     Optional[int]								=	["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
					__a       :     str								=	tokenizer.tokenize(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids without special tokens
					__a       :     Optional[Any]								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
					__a       :     Optional[int]								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids with special tokens
					__a       :     List[str]								=	tokens + [tokenizer.unk_token]
					__a       :     Dict								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
					__a       :     Any								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)
	@slow
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

					__a       :     Union[str, Any]								=	tokenizer.encode("""ありがとう。"""  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     Dict								=	tokenizer.encode("""どういたしまして。"""  ,    add_special_tokens=__UpperCamelCase			)

					__a       :     Optional[Any]								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase			)
					__a       :     int								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase  ,    __UpperCamelCase			)

					assert encoded_sentence == ids_a
					assert encoded_pair == ids_a + ids_a
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass