



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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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






					def __init__(     self					:       Dict				,					_snake_case					:       TransformeraDModel				,					_snake_case					:       AutoencoderKL				,					_snake_case					:       KarrasDiffusionSchedulers				,					_snake_case					:       Optional[Dict[int, str]] = None				,					):






								"""simple docstring"""


								super().__init__()
								self.register_modules(transformer=_snake_case				,					vae=_snake_case				,					scheduler=_snake_case					)

								# create a imagenet -> id dictionary for easier use
								A__       =    {}
								if idalabel is not None:
											for key, value in idalabel.items():
														for label in value.split(','					):
																	A__       =    int(_snake_case					)
											A__       =    dict(sorted(self.labels.items()					)					)



					def     _a							(     self					:       int				,					_snake_case					:       Union[str, List[str]]					):






								"""simple docstring"""


								if not isinstance(_snake_case				,					_snake_case					):
											A__       =    list(_snake_case					)

								for l in label:
											if l not in self.labels:
														raise ValueError(
														    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''					)

								return [self.labels[l] for l in label]



					@torch.no_grad()
					def __call__(     self					:       List[Any]				,					_snake_case					:       List[int]				,					_snake_case					:       float = 4.0				,					_snake_case					:       Optional[Union[torch.Generator, List[torch.Generator]]] = None				,					_snake_case					:       int = 50				,					_snake_case					:       Optional[str] = "pil"				,					_snake_case					:       bool = True				,					):






								"""simple docstring"""


								A__       =    len(_snake_case					)
								A__       =    self.transformer.config.sample_size
								A__       =    self.transformer.config.in_channels

								A__       =    randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)				,					generator=_snake_case				,					device=self.device				,					dtype=self.transformer.dtype				,					)
								A__       =    torch.cat([latents] * 2					) if guidance_scale > 1 else latents

								A__       =    torch.tensor(_snake_case				,					device=self.device					).reshape(-1					)
								A__       =    torch.tensor([10_00] * batch_size				,					device=self.device					)
								A__       =    torch.cat([class_labels, class_null]				,					0					) if guidance_scale > 1 else class_labels

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

								for t in self.progress_bar(self.scheduler.timesteps					):
											if guidance_scale > 1:
														A__       =    latent_model_input[: len(_snake_case					) // 2]
														A__       =    torch.cat([half, half]				,					dim=0					)
											A__       =    self.scheduler.scale_model_input(_snake_case				,					_snake_case					)

											A__       =    t
											if not torch.is_tensor(_snake_case					):
														# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
														# This would be a good case for the `match` statement (Python 3.10+)
														A__       =    latent_model_input.device.type == 'mps'
														if isinstance(_snake_case				,					_snake_case					):
																	A__       =    torch.floataa if is_mps else torch.floataa
														else:
																	A__       =    torch.intaa if is_mps else torch.intaa
														A__       =    torch.tensor([timesteps]				,					dtype=_snake_case				,					device=latent_model_input.device					)
											elif len(timesteps.shape					) == 0:
														A__       =    timesteps[None].to(latent_model_input.device					)
											# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
											A__       =    timesteps.expand(latent_model_input.shape[0]					)
											# predict noise model_output
											A__       =    self.transformer(
											    _snake_case				,					timestep=_snake_case				,					class_labels=_snake_case					).sample

											# perform guidance
											if guidance_scale > 1:
														A__     , A__       =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
														A__     , A__       =    torch.split(_snake_case				,					len(_snake_case					) // 2				,					dim=0					)

														A__       =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
														A__       =    torch.cat([half_eps, half_eps]				,					dim=0					)

														A__       =    torch.cat([eps, rest]				,					dim=1					)

											# learned sigma
											if self.transformer.config.out_channels // 2 == latent_channels:
														A__     , A__       =    torch.split(_snake_case				,					_snake_case				,					dim=1					)
											else:
														A__       =    noise_pred

											# compute previous image: x_t -> x_t-1
											A__       =    self.scheduler.step(_snake_case				,					_snake_case				,					_snake_case					).prev_sample

								if guidance_scale > 1:
											A__     , A__       =    latent_model_input.chunk(2				,					dim=0					)
								else:
											A__       =    latent_model_input

								A__       =    1 / self.vae.config.scaling_factor * latents
								A__       =    self.vae.decode(_snake_case					).sample

								A__       =    (samples / 2 + 0.5).clamp(0				,					1					)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								A__       =    samples.cpu().permute(0				,					2				,					3				,					1					).float().numpy()

								if output_type == "pil":
											A__       =    self.numpy_to_pil(_snake_case					)

								if not return_dict:
											return (samples,)

								return ImagePipelineOutput(images=_snake_case					)









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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							SCREAMING_SNAKE_CASE__       =      None

SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__       =      {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__       =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__       =      {
    '''google/rembert''': 2_5_6,
}

SCREAMING_SNAKE_CASE__       =      '''▁'''


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






					A__				:		Any			       =    VOCAB_FILES_NAMES
					A__				:		str			       =    PRETRAINED_VOCAB_FILES_MAP
					A__				:		str			       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__				:		int			       =    RemBertTokenizer



					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Any=None				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Any=True				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Dict=False				,					_snake_case					:       Dict="[CLS]"				,					_snake_case					:       List[Any]="[SEP]"				,					_snake_case					:       Union[str, Any]="<unk>"				,					_snake_case					:       List[str]="[SEP]"				,					_snake_case					:       List[str]="<pad>"				,					_snake_case					:       str="[CLS]"				,					_snake_case					:       Any="[MASK]"				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								A__       =    AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					) if isinstance(_snake_case				,					_snake_case					) else mask_token

								super().__init__(
								    _snake_case				,					tokenizer_file=_snake_case				,					do_lower_case=_snake_case				,					remove_space=_snake_case				,					keep_accents=_snake_case				,					bos_token=_snake_case				,					eos_token=_snake_case				,					unk_token=_snake_case				,					sep_token=_snake_case				,					pad_token=_snake_case				,					cls_token=_snake_case				,					mask_token=_snake_case				,					**_snake_case				,					)

								A__       =    do_lower_case
								A__       =    remove_space
								A__       =    keep_accents
								A__       =    vocab_file
								A__       =    False if not self.vocab_file else True



					def     _a							(     self					:       Any				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]
								if token_ids_a is None:
											return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



					def     _a							(     self					:       Tuple				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None				,					_snake_case					:       bool = False					):






								"""simple docstring"""


								if already_has_special_tokens:
											if token_ids_a is not None:
														raise ValueError(
														    'You should not supply a second sequence if the provided sequence of '
														    'ids is already formatted with special tokens for the model.'					)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
											return [1] + ([0] * len(_snake_case					)) + [1] + ([0] * len(_snake_case					)) + [1]
								return [1] + ([0] * len(_snake_case					)) + [1]



					def     _a							(     self					:       Dict				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



					def     _a							(     self					:       Any				,					_snake_case					:       str				,					_snake_case					:       Optional[str] = None					):






								"""simple docstring"""


								if not os.path.isdir(_snake_case					):
											logger.error('Vocabulary path ({}) should be a directory'.format(_snake_case					)					)
											return
								A__       =    os.path.join(
								    _snake_case				,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(_snake_case					):
											copyfile(self.vocab_file				,					_snake_case					)

								return (out_vocab_file,)




