
"""simple docstring"""



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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



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

class        _SCREAMING_SNAKE_CASE  (  __UpperCAmelCase							):






				'''simple docstring'''



				def __init__(					self	:			Any				,     UpperCAmelCase_	:			VQModel				,     UpperCAmelCase_	:			UNetaDModel				,     UpperCAmelCase_	:			Union[
				        DDIMScheduler,
				        PNDMScheduler,
				        LMSDiscreteScheduler,
				        EulerDiscreteScheduler,
				        EulerAncestralDiscreteScheduler,
				        DPMSolverMultistepScheduler,
				    ]				,     )		->  int:







											"""simple docstring"""




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







				@torch.no_grad()
				def __call__(					self	:			List[Any]				,     UpperCAmelCase_	:			Union[torch.Tensor, PIL.Image.Image] = None				,     UpperCAmelCase_	:			Optional[int] = 1				,     UpperCAmelCase_	:			Optional[int] = 100				,     UpperCAmelCase_	:			Optional[float] = 0.0				,     UpperCAmelCase_	:			Optional[Union[torch.Generator, List[torch.Generator]]] = None				,     UpperCAmelCase_	:			Optional[str] = "pil"				,     UpperCAmelCase_	:			bool = True				,     )		->  Dict:







											"""simple docstring"""




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

											if isinstance(__SCREAMING_SNAKE_CASE				,     PIL.Image.Image  ):
																		_lowerCAmelCase  	=			preprocess(__SCREAMING_SNAKE_CASE  )

											_lowerCAmelCase	,		_lowerCAmelCase  	=			image.shape[-2:]

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

											_lowerCAmelCase  	=			randn_tensor(__SCREAMING_SNAKE_CASE				,     generator=__SCREAMING_SNAKE_CASE				,     device=self.device				,     dtype=__SCREAMING_SNAKE_CASE  )

											_lowerCAmelCase  	=			image.to(device=self.device				,     dtype=__SCREAMING_SNAKE_CASE  )

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

											# scale the initial noise by the standard deviation required by the scheduler
											_lowerCAmelCase  	=			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]
											_lowerCAmelCase  	=			'eta' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
											_lowerCAmelCase  	=			{}
											if accepts_eta:
																		_lowerCAmelCase  	=			eta

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

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

											if output_type == "pil":
																		_lowerCAmelCase  	=			self.numpy_to_pil(__SCREAMING_SNAKE_CASE  )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE  )








"""simple docstring"""



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					None
				SCREAMING_SNAKE_CASE_:							List[str]							=					BloomTokenizerFast
				SCREAMING_SNAKE_CASE_:							List[str]							=					BloomTokenizerFast
				SCREAMING_SNAKE_CASE_:							Any							=					True
				SCREAMING_SNAKE_CASE_:							List[Any]							=					False
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					"tokenizer_file"
				SCREAMING_SNAKE_CASE_:							str							=					{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  str:







											"""simple docstring"""




											super().setUp()
											_lowerCAmelCase  	=			BloomTokenizerFast.from_pretrained('bigscience/tokenizer'  )
											tokenizer.save_pretrained(self.tmpdirname  )


				def 				__lowerCamelCase	(					self	:			List[str]				,     **UpperCAmelCase_	:			Any  )		->  Optional[Any]:







											"""simple docstring"""




											kwargs.update(self.special_tokens_map  )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_rust_tokenizer()

											_lowerCAmelCase  	=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											_lowerCAmelCase  	=			[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

											_lowerCAmelCase  	=			tokenizer.batch_encode_plus(UpperCAmelCase_  )['input_ids']
											self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			tokenizer.batch_decode(UpperCAmelCase_  )
											self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			List[str]=6  )		->  Any:







											"""simple docstring"""




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																									_lowerCAmelCase  	=			self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_  )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									_lowerCAmelCase  	=			'This is a simple input'
																									_lowerCAmelCase  	=			['This is a simple input 1', 'This is a simple input 2']
																									_lowerCAmelCase  	=			('This is a simple input', 'This is a pair')
																									_lowerCAmelCase  	=			[
																									    ('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
																									try:
																																tokenizer_r.encode(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )
																																tokenizer_r.encode_plus(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )

																																tokenizer_r.batch_encode_plus(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )
																																tokenizer_r.encode(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )
																																tokenizer_r.batch_encode_plus(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'  )

																									_lowerCAmelCase  	=			None  # Hotfixing padding = None
																									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 				__lowerCamelCase	(					self	:			str  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_rust_tokenizer()
											_lowerCAmelCase  	=			load_dataset('xnli'				,     'all_languages'				,     split='test'				,     streaming=UpperCAmelCase_  )

											_lowerCAmelCase  	=			next(iter(UpperCAmelCase_  )  )['premise']  # pick up one data
											_lowerCAmelCase  	=			list(sample_data.values()  )

											_lowerCAmelCase  	=			list(map(tokenizer.encode				,     UpperCAmelCase_  )  )
											_lowerCAmelCase  	=			[tokenizer.decode(UpperCAmelCase_				,     clean_up_tokenization_spaces=UpperCAmelCase_  ) for x in output_tokens]
											self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			Tuple  )		->  List[Any]:







											"""simple docstring"""




											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map  )				,     1  )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()  )[0]  )				,     1  )






