

'''simple docstring'''





import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 						__lowercase       (				SCREAMING_SNAKE_CASE__	, unittest.TestCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							Dict    							=  MPNetTokenizer
			_UpperCAmelCase     :							Tuple    							=  MPNetTokenizerFast
			_UpperCAmelCase     :							Optional[int]    							=  True
			_UpperCAmelCase     :							Optional[Any]    							=  True



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									super().setUp()

									SCREAMING_SNAKE_CASE_:				Tuple							= [
									    "[UNK]",
									    "[CLS]",
									    "[SEP]",
									    "[PAD]",
									    "[MASK]",
									    "want",
									    "##want",
									    "##ed",
									    "wa",
									    "un",
									    "runn",
									    "##ing",
									    ",",
									    "low",
									    "lowest",
									]
									SCREAMING_SNAKE_CASE_:				Any							= os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["vocab_file"])
									with open(self.vocab_file     ,		"w"     ,		encoding="utf-8") as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		lowerCAmelCase__						:  int):
									SCREAMING_SNAKE_CASE_:				int							= "UNwant\u00E9d,running"
									SCREAMING_SNAKE_CASE_:				Tuple							= "unwanted, running"
									return input_text, output_text



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									SCREAMING_SNAKE_CASE_:				str							= self.tokenizer_class(self.vocab_file)

									SCREAMING_SNAKE_CASE_:				List[Any]							= tokenizer.tokenize("UNwant\u00E9d,running")
									self.assertListEqual(a_     ,		["un", "##want", "##ed", ",", "runn", "##ing"])
									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_)     ,		[9, 6, 7, 12, 10, 11])




			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				List[Any]							= self.tokenizer_class.from_pretrained("microsoft/mpnet-base")

									SCREAMING_SNAKE_CASE_:				str							= tokenizer.encode("sequence builders"     ,		add_special_tokens=a_)
									SCREAMING_SNAKE_CASE_:				Any							= tokenizer.encode("multi-sequence build"     ,		add_special_tokens=a_)

									SCREAMING_SNAKE_CASE_:				List[Any]							= tokenizer.build_inputs_with_special_tokens(a_)
									SCREAMING_SNAKE_CASE_:				List[Any]							= tokenizer.build_inputs_with_special_tokens(a_     ,		a_)

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









from typing import List, Optional, Tuple, Union

import torch

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



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

									SCREAMING_SNAKE_CASE_:				Dict							= PNDMScheduler.from_config(scheduler.config)

									self.register_modules(unet=lowerCAmelCase__     ,		scheduler=lowerCAmelCase__)




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,		generator=lowerCAmelCase__     ,		device=self.device     ,		)

									self.scheduler.set_timesteps(lowerCAmelCase__)
									for t in self.progress_bar(self.scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (image / 2 + 0.5).clamp(0     ,		1)
									SCREAMING_SNAKE_CASE_:				Dict							= image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:				Any							= self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






