




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_      (   A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     OpenAIGPTTokenizer
							a__						  =     OpenAIGPTTokenizerFast
							a__						  =     True
							a__						  =     False





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					List[str]:
										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       :	Tuple       ,     a       :	Union[str, Any]			)			->					int:
										return "lower newer", "lower newer"





							def    _UpperCAmelCase     (					self       :	str			)			->					Optional[Any]:
										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       :	str       ,     a       :	Dict=15			)			->					Any:
										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       :	Any			)			->					Optional[int]:
										pass





@require_ftfy
@require_spacy
@require_tokenizers
class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							pass












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     42
							a__						  =     42





							def __init__(					self       :	Any       ,     a       :	UNetaDModel       ,     a       :	ScoreSdeVeScheduler			)			->					str:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a			)







							@torch.no_grad()
							def __call__(					self       :	List[Any]       ,     a       :	int = 1       ,     a       :	int = 2_000       ,     a       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     a       :	Optional[str] = "pil"       ,     a       :	bool = True       ,     **a       :	Optional[int]       ,     )			->					Union[ImagePipelineOutput, Tuple]:
										SCREAMING_SNAKE_CASE           =					self.unet.config.sample_size
										SCREAMING_SNAKE_CASE           =					(batch_size, 3, img_size, img_size)

										SCREAMING_SNAKE_CASE           =					self.unet

										SCREAMING_SNAKE_CASE           =					randn_tensor(a       ,     generator=a			) * self.scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE           =					sample.to(self.device			)

										self.scheduler.set_timesteps(a			)
										self.scheduler.set_sigmas(a			)

										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
													SCREAMING_SNAKE_CASE           =					self.scheduler.sigmas[i] * torch.ones(shape[0]       ,     device=self.device			)

													# correction step
													for _ in range(self.scheduler.config.correct_steps			):
																SCREAMING_SNAKE_CASE           =					self.unet(a       ,     a			).sample
																SCREAMING_SNAKE_CASE           =					self.scheduler.step_correct(a       ,     a       ,     generator=a			).prev_sample

													# prediction step
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample
													SCREAMING_SNAKE_CASE           =					self.scheduler.step_pred(a       ,     a       ,     a       ,     generator=a			)

													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					output.prev_sample, output.prev_sample_mean

										SCREAMING_SNAKE_CASE           =					sample_mean.clamp(0       ,     1			)
										SCREAMING_SNAKE_CASE           =					sample.cpu().permute(0       ,     2       ,     3       ,     1			).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE           =					self.numpy_to_pil(a			)

										if not return_dict:
													return (sample,)

										return ImagePipelineOutput(images=a			)






