

from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			)       ->       Optional[Any]:
												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_a      :       List[str]   =						DDIMScheduler.from_config(scheduler.config			)

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




							@torch.no_grad()
							def __call__(     self			,				_a = 1			,				_a = None			,				_a = 0.0			,				_a = 5_0			,				_a = None			,				_a = "pil"			,				_a = True			,				)       ->       Union[ImagePipelineOutput, Tuple]:

												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size			,				_a			):
																	_a      :       str   =						(
																	    batch_size,
																	    self.unet.config.in_channels,
																	    self.unet.config.sample_size,
																	    self.unet.config.sample_size,
																	)
												else:
																	_a      :       Optional[int]   =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(_a			,				_a			) and len(_a			) != batch_size:
																	raise ValueError(
																	    F"""You have passed a list of generators of length {len(_a			)}, but requested an effective batch"""
																	    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""			)

												_a      :       int   =						randn_tensor(_a			,				generator=_a			,				device=self.device			,				dtype=self.unet.dtype			)

												# set step values
												self.scheduler.set_timesteps(_a			)

												for t in self.progress_bar(self.scheduler.timesteps			):
																	# 1. predict noise model_output
																	_a      :       Any   =						self.unet(_a			,				_a			).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	_a      :       int   =						self.scheduler.step(
																	    _a			,				_a			,				_a			,				eta=_a			,				use_clipped_model_output=_a			,				generator=_a			).prev_sample

												_a      :       int   =						(image / 2 + 0.5).clamp(0			,				1			)
												_a      :       Dict   =						image.cpu().permute(0			,				2			,				3			,				1			).numpy()
												if output_type == "pil":
																	_a      :       List[str]   =						self.numpy_to_pil(_a			)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_a			)






import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       LxmertTokenizer
							UpperCAmelCase__			:		Optional[Any]             =       LxmertTokenizerFast
							UpperCAmelCase__			:		Any             =       True
							UpperCAmelCase__			:		Dict             =       True


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												super().setUp()

												_a      :       int   =						[
												    '''[UNK]''',
												    '''[CLS]''',
												    '''[SEP]''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												_a      :       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 							__lowercase							(     self			,				_a			)       ->       List[str]:
												_a      :       Tuple   =						'''UNwant\u00E9d,running'''
												_a      :       str   =						'''unwanted, running'''
												return input_text, output_text


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						self.tokenizer_class(self.vocab_file			)

												_a      :       str   =						tokenizer.tokenize('''UNwant\u00E9d,running'''			)
												self.assertListEqual(_a			,				['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a			)			,				[7, 4, 5, 1_0, 8, 9]			)




							def 							__lowercase							(     self			)       ->       List[Any]:
												if not self.test_rust_tokenizer:
																	return

												_a      :       Optional[Any]   =						self.get_tokenizer()
												_a      :       str   =						self.get_rust_tokenizer()

												_a      :       Optional[Any]   =						'''I was born in 92000, and this is falsé.'''

												_a      :       Optional[Any]   =						tokenizer.tokenize(_a			)
												_a      :       List[Any]   =						rust_tokenizer.tokenize(_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       List[Any]   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
												_a      :       Any   =						rust_tokenizer.encode(_a			,				add_special_tokens=_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       Dict   =						self.get_rust_tokenizer()
												_a      :       Optional[int]   =						tokenizer.encode(_a			)
												_a      :       Dict   =						rust_tokenizer.encode(_a			)
												self.assertListEqual(_a			,				_a			)



