




from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


UpperCAmelCase__				     = logging.get_logger(__name__)  # pylint: disable=invalid-name







class a (     lowercase_  ):
						"""simple docstring"""

						def __init__( self :     Dict				,							lowerCamelCase__ :     CLIPSegForImageSegmentation				,							lowerCamelCase__ :     CLIPSegProcessor				,							lowerCamelCase__ :     AutoencoderKL				,							lowerCamelCase__ :     CLIPTextModel				,							lowerCamelCase__ :     CLIPTokenizer				,							lowerCamelCase__ :     UNetaDConditionModel				,							lowerCamelCase__ :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,							lowerCamelCase__ :     StableDiffusionSafetyChecker				,							lowerCamelCase__ :     CLIPImageProcessor				,							)    ->						str:
													"""simple docstring"""


													super().__init__()

													if hasattr(scheduler.config				,							'''steps_offset'''				) and scheduler.config.steps_offset != 1:
																				__lowercase      =    (
																				    f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
																				    f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
																				    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
																				    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
																				    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
																				    ''' file'''
																				)
																				deprecate('''steps_offset!=1'''				,							'''1.0.0'''				,							UpperCamelCase__				,							standard_warn=UpperCamelCase__				)
																				__lowercase      =    dict(scheduler.config				)
																				__lowercase      =    1
																				__lowercase      =    FrozenDict(UpperCamelCase__				)

													if hasattr(scheduler.config				,							'''skip_prk_steps'''				) and scheduler.config.skip_prk_steps is False:
																				__lowercase      =    (
																				    f'The configuration file of this scheduler: {scheduler} has not set the configuration'
																				    ''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
																				    ''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'''
																				    ''' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'''
																				    ''' Hub, it would be very nice if you could open a Pull request for the'''
																				    ''' `scheduler/scheduler_config.json` file'''
																				)
																				deprecate('''skip_prk_steps not set'''				,							'''1.0.0'''				,							UpperCamelCase__				,							standard_warn=UpperCamelCase__				)
																				__lowercase      =    dict(scheduler.config				)
																				__lowercase      =    True
																				__lowercase      =    FrozenDict(UpperCamelCase__				)

													if safety_checker is None:
																				logger.warning(
																				    f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
																				    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
																				    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
																				    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
																				    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
																				    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''				)

													self.register_modules(
													    segmentation_model=UpperCamelCase__				,							segmentation_processor=UpperCamelCase__				,							vae=UpperCamelCase__				,							text_encoder=UpperCamelCase__				,							tokenizer=UpperCamelCase__				,							unet=UpperCamelCase__				,							scheduler=UpperCamelCase__				,							safety_checker=UpperCamelCase__				,							feature_extractor=UpperCamelCase__				,							)






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     Optional[Union[str, int]] = "auto"				)    ->						List[Any]:
													"""simple docstring"""


													if slice_size == "auto":
																				# half the attention head size is usually a good trade-off between
																				# speed and memory
																				__lowercase      =    self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(UpperCamelCase__				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						int:
													"""simple docstring"""


													self.enable_attention_slicing(UpperCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						str:
													"""simple docstring"""


													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

													__lowercase      =    torch.device('''cuda'''				)

													for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																				if cpu_offloaded_model is not None:
																											cpu_offload(UpperCamelCase__				,							UpperCamelCase__				)






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     UpperCAmelCase_      ( self :     Any				)    ->						Any:
													"""simple docstring"""


													if self.device != torch.device('''meta'''				) or not hasattr(self.unet				,							'''_hf_hook'''				):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(UpperCamelCase__				,							'''_hf_hook'''				)
																				    and hasattr(module._hf_hook				,							'''execution_device'''				)
																				    and module._hf_hook.execution_device is not None
																				):
																											return torch.device(module._hf_hook.execution_device				)
													return self.device






						@torch.no_grad()
						def __call__( self :     Optional[Any]				,							lowerCamelCase__ :     Union[str, List[str]]				,							lowerCamelCase__ :     Union[torch.FloatTensor, PIL.Image.Image]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     int = 512				,							lowerCamelCase__ :     int = 512				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     float = 7.5				,							lowerCamelCase__ :     Optional[Union[str, List[str]]] = None				,							lowerCamelCase__ :     Optional[int] = 1				,							lowerCamelCase__ :     float = 0.0				,							lowerCamelCase__ :     Optional[torch.Generator] = None				,							lowerCamelCase__ :     Optional[torch.FloatTensor] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							lowerCamelCase__ :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,							lowerCamelCase__ :     int = 1				,							**lowerCamelCase__ :     Optional[Any]				,							)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    self.segmentation_processor(
													    text=[text]				,							images=[image]				,							padding='''max_length'''				,							return_tensors='''pt'''				).to(self.device				)
													__lowercase      =    self.segmentation_model(**UpperCamelCase__				)
													__lowercase      =    torch.sigmoid(outputs.logits				).cpu().detach().unsqueeze(-1				).numpy()
													__lowercase      =    self.numpy_to_pil(UpperCamelCase__				)[0].resize(image.size				)

													# Run inpainting pipeline with the generated mask
													__lowercase      =    StableDiffusionInpaintPipeline(
													    vae=self.vae				,							text_encoder=self.text_encoder				,							tokenizer=self.tokenizer				,							unet=self.unet				,							scheduler=self.scheduler				,							safety_checker=self.safety_checker				,							feature_extractor=self.feature_extractor				,							)
													return inpainting_pipeline(
													    prompt=UpperCamelCase__				,							image=UpperCamelCase__				,							mask_image=UpperCamelCase__				,							height=UpperCamelCase__				,							width=UpperCamelCase__				,							num_inference_steps=UpperCamelCase__				,							guidance_scale=UpperCamelCase__				,							negative_prompt=UpperCamelCase__				,							num_images_per_prompt=UpperCamelCase__				,							eta=UpperCamelCase__				,							generator=UpperCamelCase__				,							latents=UpperCamelCase__				,							output_type=UpperCamelCase__				,							return_dict=UpperCamelCase__				,							callback=UpperCamelCase__				,							callback_steps=UpperCamelCase__				,							)












import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__				     = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")







@require_sentencepiece
@require_tokenizers
class a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		GPTSwaTokenizer
						UpperCamelCase_	:       Union[str, Any]       	=		False
						UpperCamelCase_	:       Union[str, Any]       	=		True
						UpperCamelCase_	:       Optional[Any]       	=		False






						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													super().setUp()

													# We have a SentencePiece fixture for testing
													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				,							eos_token='''<unk>'''				,							bos_token='''<unk>'''				,							pad_token='''<unk>'''				)

													tokenizer.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Dict				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    '''This is a test'''
													__lowercase      =    '''This is a test'''
													return input_text, output_text






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    '''<s>'''
													__lowercase      =    1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__				)				,							lowerCamelCase__				)
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__				)				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    list(self.get_tokenizer().get_vocab().keys()				)

													self.assertEqual(vocab_keys[0]				,							'''<unk>'''				)
													self.assertEqual(vocab_keys[1]				,							'''<s>'''				)
													self.assertEqual(vocab_keys[-1]				,							'''j'''				)
													self.assertEqual(len(lowerCamelCase__				)				,							2_000				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						str:
													"""simple docstring"""


													self.assertEqual(self.get_tokenizer().vocab_size				,							2_000				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)

													__lowercase      =    tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCamelCase__				,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)				,							[465, 287, 265, 631, 842]				)

													__lowercase      =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				,							)
													# fmt: on

													__lowercase      =    tokenizer.convert_tokens_to_ids(lowerCamelCase__				)
													self.assertListEqual(
													    lowerCamelCase__				,							[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]				,							)

													__lowercase      =    tokenizer.convert_ids_to_tokens(lowerCamelCase__				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				)
													# fmt: on






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)
													__lowercase      =    ['''This is a test''', '''I was born in 92000, and this is falsé.''']
													__lowercase      =    [
													    [465, 287, 265, 631, 842],
													    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
													]

													# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
													for text, expected_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertListEqual(tokenizer.encode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)

													# Test that decode_fast returns the input text
													for text, token_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertEqual(tokenizer.decode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)






						@slow
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""simple docstring"""


													__lowercase      =    [
													    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
													    '''Hey there, how are you doing this fine day?''',
													    '''This is a text with a trailing spaces followed by a dot     .''',
													    '''Häj sväjs lillebrör! =)''',
													    '''Det är inget fel på Mr. Cool''',
													]

													# fmt: off
													__lowercase      =    {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
													# fmt: on
													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCamelCase__				,							model_name='''AI-Sweden/gpt-sw3-126m'''				,							sequences=lowerCamelCase__				,							)






