






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


a_							=    logging.get_logger(__name__)

a_							=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a_							=    {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

a_							=    {
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}


class      lowercase__     (			_UpperCAmelCase	):
	a_         =VOCAB_FILES_NAMES
	a_         =PRETRAINED_VOCAB_FILES_MAP
	a_         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a_         =["""input_ids""", """attention_mask"""]
	a_         =RobertaTokenizer

	def __init__(							self		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    __UpperCAmelCase="replace"		,    __UpperCAmelCase="<s>"		,    __UpperCAmelCase="</s>"		,    __UpperCAmelCase="</s>"		,    __UpperCAmelCase="<s>"		,    __UpperCAmelCase="<unk>"		,    __UpperCAmelCase="<pad>"		,    __UpperCAmelCase="<mask>"		,    __UpperCAmelCase=False		,    __UpperCAmelCase=True		,    **__UpperCAmelCase		,    )->					Optional[Any]:







							'''simple docstring'''





							super().__init__(
							    __UpperCAmelCase		,    __UpperCAmelCase		,    tokenizer_file=__UpperCAmelCase		,    errors=__UpperCAmelCase		,    bos_token=__UpperCAmelCase		,    eos_token=__UpperCAmelCase		,    sep_token=__UpperCAmelCase		,    cls_token=__UpperCAmelCase		,    unk_token=__UpperCAmelCase		,    pad_token=__UpperCAmelCase		,    mask_token=__UpperCAmelCase		,    add_prefix_space=__UpperCAmelCase		,    trim_offsets=__UpperCAmelCase		,    **__UpperCAmelCase		,    )

							lowerCAmelCase__       =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
							if pre_tok_state.get("add_prefix_space"		,    __UpperCAmelCase       ) != add_prefix_space:
													lowerCAmelCase__       =						getattr(__UpperCAmelCase		,    pre_tok_state.pop("type"       )       )
													lowerCAmelCase__       =						add_prefix_space
													lowerCAmelCase__       =						pre_tok_class(**__UpperCAmelCase       )

							lowerCAmelCase__       =						add_prefix_space

							lowerCAmelCase__       =						"post_processor"
							lowerCAmelCase__       =						getattr(self.backend_tokenizer		,    __UpperCAmelCase		,    __UpperCAmelCase       )
							if tokenizer_component_instance:
													lowerCAmelCase__       =						json.loads(tokenizer_component_instance.__getstate__()       )

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																			lowerCAmelCase__       =						tuple(state["sep"]       )
													if "cls" in state:
																			lowerCAmelCase__       =						tuple(state["cls"]       )

													lowerCAmelCase__       =						False

													if state.get("add_prefix_space"		,    __UpperCAmelCase       ) != add_prefix_space:
																			lowerCAmelCase__       =						add_prefix_space
																			lowerCAmelCase__       =						True

													if state.get("trim_offsets"		,    __UpperCAmelCase       ) != trim_offsets:
																			lowerCAmelCase__       =						trim_offsets
																			lowerCAmelCase__       =						True

													if changes_to_apply:
																			lowerCAmelCase__       =						getattr(__UpperCAmelCase		,    state.pop("type"       )       )
																			lowerCAmelCase__       =						component_class(**__UpperCAmelCase       )
																			setattr(self.backend_tokenizer		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	@property
	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							if self._mask_token is None:
													if self.verbose:
																			logger.error("Using mask_token, but it is not set yet."       )
													return None
							return str(self._mask_token       )

	@mask_token.setter
	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						AddedToken(__UpperCAmelCase		,    lstrip=__UpperCAmelCase		,    rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else value
							lowerCAmelCase__       =						value

	def 		UpperCAmelCase  (							self		,    *__UpperCAmelCase		,    **__UpperCAmelCase       )->					BatchEncoding:







							'''simple docstring'''





							lowerCAmelCase__       =						kwargs.get("is_split_into_words"		,    __UpperCAmelCase       )
							assert self.add_prefix_space or not is_split_into_words, (
							    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*__UpperCAmelCase		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    *__UpperCAmelCase		,    **__UpperCAmelCase       )->					BatchEncoding:







							'''simple docstring'''





							lowerCAmelCase__       =						kwargs.get("is_split_into_words"		,    __UpperCAmelCase       )

							assert self.add_prefix_space or not is_split_into_words, (
							    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*__UpperCAmelCase		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None       )->					Tuple[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						self._tokenizer.model.save(__UpperCAmelCase		,    name=__UpperCAmelCase       )
							return tuple(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=None       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
													return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None       )->					List[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						[self.sep_token_id]
							lowerCAmelCase__       =						[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 + sep + token_ids_a + sep       ) * [0]














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      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    )->					List[str]:







							'''simple docstring'''





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

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[int]:







							'''simple docstring'''





							if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						list(__UpperCAmelCase       )

							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		,    __UpperCAmelCase		,    __UpperCAmelCase = 4.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    )->					Union[ImagePipelineOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						len(__UpperCAmelCase       )
							lowerCAmelCase__       =						self.transformer.config.sample_size
							lowerCAmelCase__       =						self.transformer.config.in_channels

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

							lowerCAmelCase__       =						torch.tensor(__UpperCAmelCase		,    device=self.device       ).reshape(-1       )
							lowerCAmelCase__       =						torch.tensor([1000] * batch_size		,    device=self.device       )
							lowerCAmelCase__       =						torch.cat([class_labels, class_null]		,    0       ) if guidance_scale > 1 else class_labels

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

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

													lowerCAmelCase__       =						t
													if not torch.is_tensor(__UpperCAmelCase       ):
																			# 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+)
																			lowerCAmelCase__       =						latent_model_input.device.type == "mps"
																			if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
																									lowerCAmelCase__       =						torch.floataa if is_mps else torch.floataa
																			else:
																									lowerCAmelCase__       =						torch.intaa if is_mps else torch.intaa
																			lowerCAmelCase__       =						torch.tensor([timesteps]		,    dtype=__UpperCAmelCase		,    device=latent_model_input.device       )
													elif len(timesteps.shape       ) == 0:
																			lowerCAmelCase__       =						timesteps[None].to(latent_model_input.device       )
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													lowerCAmelCase__       =						timesteps.expand(latent_model_input.shape[0]       )
													# predict noise model_output
													lowerCAmelCase__       =						self.transformer(
													    __UpperCAmelCase		,    timestep=__UpperCAmelCase		,    class_labels=__UpperCAmelCase       ).sample

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

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

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

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

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

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

							lowerCAmelCase__       =						1 / self.vae.config.scaling_factor * latents
							lowerCAmelCase__       =						self.vae.decode(__UpperCAmelCase       ).sample

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

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

							if output_type == "pil":
													lowerCAmelCase__       =						self.numpy_to_pil(__UpperCAmelCase       )

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=__UpperCAmelCase       )






