




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def 		_A(       UpperCamelCase__							:			Tuple				)    -> List[str]:
							'''simple docstring'''


							__lowercase      =    image.size
							__lowercase      =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
							__lowercase      =    image.resize((w, h)				,       resample=PIL_INTERPOLATION['''lanczos''']				)
							__lowercase      =    np.array(__UpperCamelCase				).astype(np.floataa				) / 255.0
							__lowercase      =    image[None].transpose(0				,       3				,       1				,       2				)
							__lowercase      =    torch.from_numpy(__UpperCamelCase				)
							return 2.0 * image - 1.0







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

						def __init__( self :     Optional[Any]				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     int				,							)    ->						str:
													"""simple docstring"""


													super().__init__()
													self.register_modules(vqvae=UpperCamelCase__				,							unet=UpperCamelCase__				,							scheduler=UpperCamelCase__				)






						@torch.no_grad()
						def __call__( self :     Tuple				,							lowerCamelCase__ :     Optional[Any] = None				,							lowerCamelCase__ :     str = 1				,							lowerCamelCase__ :     Dict = 100				,							lowerCamelCase__ :     str = 0.0				,							lowerCamelCase__ :     Dict = None				,							lowerCamelCase__ :     Optional[int] = "pil"				,							lowerCamelCase__ :     int = True				,							)    ->						List[Any]:
													"""simple docstring"""


													if isinstance(UpperCamelCase__				,							PIL.Image.Image				):
																				__lowercase      =    1
													elif isinstance(UpperCamelCase__				,							torch.Tensor				):
																				__lowercase      =    image.shape[0]
													else:
																				raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__				)}'				)

													if isinstance(UpperCamelCase__				,							PIL.Image.Image				):
																				__lowercase      =    preprocess(UpperCamelCase__				)

													__lowercase      =    image.shape[-2:]

													# in_channels should be 6: 3 for latents, 3 for low resolution image
													__lowercase      =    (batch_size, self.unet.config.in_channels // 2, height, width)
													__lowercase      =    next(self.unet.parameters()				).dtype

													__lowercase      =    randn_tensor(UpperCamelCase__				,							generator=UpperCamelCase__				,							device=self.device				,							dtype=UpperCamelCase__				)

													__lowercase      =    image.to(device=self.device				,							dtype=UpperCamelCase__				)

													# set timesteps and move to the correct device
													self.scheduler.set_timesteps(UpperCamelCase__				,							device=self.device				)
													__lowercase      =    self.scheduler.timesteps

													# scale the initial noise by the standard deviation required by the scheduler
													__lowercase      =    latents * self.scheduler.init_noise_sigma

													# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
													# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
													# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
													# and should be between [0, 1]
													__lowercase      =    '''eta''' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
													__lowercase      =    {}
													if accepts_eta:
																				__lowercase      =    eta

													for t in self.progress_bar(UpperCamelCase__				):
																				# concat latents and low resolution image in the channel dimension.
																				__lowercase      =    torch.cat([latents, image]				,							dim=1				)
																				__lowercase      =    self.scheduler.scale_model_input(UpperCamelCase__				,							UpperCamelCase__				)
																				# predict the noise residual
																				__lowercase      =    self.unet(UpperCamelCase__				,							UpperCamelCase__				).sample
																				# compute the previous noisy sample x_t -> x_t-1
																				__lowercase      =    self.scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							**UpperCamelCase__				).prev_sample

													# decode the image latents with the VQVAE
													__lowercase      =    self.vqvae.decode(UpperCamelCase__				).sample
													__lowercase      =    torch.clamp(UpperCamelCase__				,							-1.0				,							1.0				)
													__lowercase      =    image / 2 + 0.5
													__lowercase      =    image.cpu().permute(0				,							2				,							3				,							1				).numpy()

													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(UpperCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=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__				,							)






