






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Optional[Any]									=							OpenAIGPTTokenizer
			__snake_case    :				Dict									=							OpenAIGPTTokenizerFast
			__snake_case    :				Optional[Any]									=							True
			__snake_case    :				Dict									=							False







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_SCREAMING_SNAKE_CASE     				=  [
									    """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>""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  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 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									return "lower newer", "lower newer"







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OpenAIGPTTokenizer(self.vocab_file				,     self.merges_file					)

									_SCREAMING_SNAKE_CASE     				=  """lower"""
									_SCREAMING_SNAKE_CASE     				=  ["""low""", """er</w>"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokens + ["""<unk>"""]
									_SCREAMING_SNAKE_CASE     				=  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]=15					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)

																					# Simple input
																					_SCREAMING_SNAKE_CASE     				=  """This is a simple input"""
																					_SCREAMING_SNAKE_CASE     				=  ["""This is a simple input 1""", """This is a simple input 2"""]
																					_SCREAMING_SNAKE_CASE     				=  ("""This is a simple input""", """This is a pair""")
																					_SCREAMING_SNAKE_CASE     				=  [
																					    ("""This is a simple input 1""", """This is a simple input 2"""),
																					    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																					]

																					# Simple input tests
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass



@require_ftfy
@require_spacy
@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			pass









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							(UnCLIPScheduler,)







			def 							UpperCamelCase				(     self:  int				,     **UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**UpperCAmelCase_					)
									return config







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																					if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																					self.check_over_forward(time_step=UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""fixed_small_log"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000E-10					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""learned_range"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  0.5

									assert scheduler._get_variance(1				,     predicted_variance=UpperCAmelCase_					) - -10.1_71_27_90 < 1E-5
									assert scheduler._get_variance(487				,     predicted_variance=UpperCAmelCase_					) - -5.7_99_80_52 < 1E-5
									assert scheduler._get_variance(999				,     predicted_variance=UpperCAmelCase_					) - -0.0_01_00_11 < 1E-5







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

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

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

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

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
									assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									scheduler.set_timesteps(25					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

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

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															if i + 1 == timesteps.shape[0]:
																					_SCREAMING_SNAKE_CASE     				=  None
															else:
																					_SCREAMING_SNAKE_CASE     				=  timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(
															    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
									assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									pass

