


from typing import List, Optional, Tuple, Union

import torch

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





class       A    (   A_ ):
							UpperCamelCase_   :							UNetaDModel
							UpperCamelCase_   :							KarrasVeScheduler


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									super().__init__()
									self.register_modules(unet=lowerCAmelCase      ,				scheduler=lowerCAmelCase					)







							@torch.no_grad()
							def __call__(self      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = 5_0      ,				lowerCAmelCase = None      ,				lowerCAmelCase = "pil"      ,				lowerCAmelCase = True      ,				**lowerCAmelCase      ,				):
									__lowercase=			self.unet.config.sample_size
									__lowercase=			(batch_size, 3, img_size, img_size)

									__lowercase=			self.unet

									# sample x_0 ~ N(0, sigma_0^2 * I)
									__lowercase=			randn_tensor(lowerCAmelCase      ,				generator=lowerCAmelCase      ,				device=self.device					) * self.scheduler.init_noise_sigma

									self.scheduler.set_timesteps(lowerCAmelCase					)

									for t in self.progress_bar(self.scheduler.timesteps					):
											# here sigma_t == t_i from the paper
											__lowercase=			self.scheduler.schedule[t]
											__lowercase=			self.scheduler.schedule[t - 1] if t > 0 else 0

											# 1. Select temporarily increased noise level sigma_hat
											# 2. Add new noise to move from sample_i to sample_hat
											__lowercase,							__lowercase=			self.scheduler.add_noise_to_input(lowerCAmelCase      ,				lowerCAmelCase      ,				generator=lowerCAmelCase					)

											# 3. Predict the noise residual given the noise magnitude `sigma_hat`
											# The model inputs and output are adjusted by following eq. (213) in [1].
											__lowercase=			(sigma_hat / 2) * model((sample_hat + 1) / 2      ,				sigma_hat / 2					).sample

											# 4. Evaluate dx/dt at sigma_hat
											# 5. Take Euler step from sigma to sigma_prev
											__lowercase=			self.scheduler.step(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					)

											if sigma_prev != 0:
													# 6. Apply 2nd order correction
													# The model inputs and output are adjusted by following eq. (213) in [1].
													__lowercase=			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2      ,				sigma_prev / 2					).sample
													__lowercase=			self.scheduler.step_correct(
													    lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				step_output.prev_sample      ,				step_output['derivative']      ,				)
											__lowercase=			step_output.prev_sample

									__lowercase=			(sample / 2 + 0.5).clamp(0      ,				1					)
									__lowercase=			sample.cpu().permute(0      ,				2      ,				3      ,				1					).numpy()
									if output_type == "pil":
											__lowercase=			self.numpy_to_pil(lowerCAmelCase					)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase					)










import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase           =		'''▁'''

lowerCAmelCase           =		{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase           =		{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

lowerCAmelCase           =		{
    '''google/pegasus-xsum''': 5_1_2,
}


lowerCAmelCase           =		logging.get_logger(__name__)





class       A    (   A_ ):
							UpperCamelCase_   :							Union[str, Any]       =VOCAB_FILES_NAMES

							UpperCamelCase_   :							List[Any]       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							int       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							Tuple       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							int       =['''input_ids''', '''attention_mask''']


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<mask_2>"      ,				lowerCAmelCase="<mask_1>"      ,				lowerCAmelCase=None      ,				lowerCAmelCase=1_0_3      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									__lowercase=			offset
									if additional_special_tokens is not None:
											if not isinstance(lowerCAmelCase      ,				lowerCAmelCase					):
													raise TypeError(
													    f'additional_special_tokens should be of type {type(lowerCAmelCase					)}, but is'
													    f' {type(lowerCAmelCase					)}'					)

											__lowercase=			(
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    f'<unk_{i}>' for i in range(len(lowerCAmelCase					)      ,				self.offset - 1					)
											]

											if len(set(lowerCAmelCase					)					) != len(lowerCAmelCase					):
													raise ValueError(
													    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
													    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'					)
											__lowercase=			additional_special_tokens_extended
									else:
											__lowercase=			[mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [f'<unk_{i}>' for i in range(2      ,				self.offset					)]

									__lowercase=			{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=lowerCAmelCase      ,				unk_token=lowerCAmelCase      ,				mask_token=lowerCAmelCase      ,				pad_token=lowerCAmelCase      ,				mask_token_sent=lowerCAmelCase      ,				offset=lowerCAmelCase      ,				additional_special_tokens=lowerCAmelCase      ,				sp_model_kwargs=self.sp_model_kwargs      ,				**lowerCAmelCase      ,				)
									__lowercase=			mask_token_sent
									__lowercase=			vocab_file
									__lowercase=			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(lowerCAmelCase					)

									# add special tokens to encoder dict
									__lowercase=			{
									    0: self.pad_token,
									    1: self.eos_token,
									}

									if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }					)

									if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1      ,				self.offset - 1					)}					)

									__lowercase=			{v: k for k, v in self.encoder.items()}


							@property
							def 			_A       (self					):
									return len(self.sp_model					) + self.offset


							def 			_A       (self					):
									__lowercase=			{self.convert_ids_to_tokens(lowerCAmelCase					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab


							def __getstate__(self					):
									__lowercase=			self.__dict__.copy()
									__lowercase=			None
									return state


							def __setstate__(self      ,				lowerCAmelCase					):
									__lowercase=			d

									# for backward compatibility
									if not hasattr(self      ,				'sp_model_kwargs'					):
											__lowercase=			{}

									__lowercase=			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(self.vocab_file					)


							def 			_A       (self      ,				lowerCAmelCase					):
									return self.sp_model.encode(lowerCAmelCase      ,				out_type=lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase					):
									if token in self.decoder:
											return self.decoder[token]
									elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
									__lowercase=			self.sp_model.piece_to_id(lowerCAmelCase					)
									return sp_id + self.offset


							def 			_A       (self      ,				lowerCAmelCase					):
									if index in self.encoder:
											return self.encoder[index]
									elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
									else:
											__lowercase=			self.sp_model.IdToPiece(index - self.offset					)
									return token


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			[]
									__lowercase=			''
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													out_string += self.sp_model.decode(lowerCAmelCase					) + token
													__lowercase=			[]
											else:
													current_sub_tokens.append(lowerCAmelCase					)
									out_string += self.sp_model.decode(lowerCAmelCase					)
									return out_string.strip()


							def 			_A       (self      ,				lowerCAmelCase=False					):
									return 1


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			set(self.all_special_ids					)  # call it once instead of inside list comp
									all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

									return [1 if x in all_special_ids else 0 for x in seq]


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				lowerCAmelCase = False					):
									if already_has_special_tokens:
											return self._special_token_mask(lowerCAmelCase					)
									elif token_ids_a is None:
											return self._special_token_mask(lowerCAmelCase					) + [1]
									else:
											return self._special_token_mask(token_ids_a + token_ids_a					) + [1]


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=None					):
									if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return token_ids_a + token_ids_a + [self.eos_token_id]







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									if not os.path.isdir(lowerCAmelCase					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
									__lowercase=			os.path.join(
									    lowerCAmelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file      ,				lowerCAmelCase					)
									elif not os.path.isfile(self.vocab_file					):
											with open(lowerCAmelCase      ,				'wb'					) as fi:
													__lowercase=			self.sp_model.serialized_model_proto()
													fi.write(lowerCAmelCase					)

									return (out_vocab_file,)






