"""simple docstring"""


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


a							=logging.get_logger(__name__)

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

a							={
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

a							={
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

a							={
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Any  							=       VOCAB_FILES_NAMES
				A__      :   str  							=       PRETRAINED_VOCAB_FILES_MAP
				A__      :   Union[str, Any]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__      :   Union[str, Any]  							=       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
				A__      :   List[str]  							=       DebertaTokenizer

				def __init__(      self	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase="replace"	, _lowerCamelCase="[CLS]"	, _lowerCamelCase="[SEP]"	, _lowerCamelCase="[SEP]"	, _lowerCamelCase="[CLS]"	, _lowerCamelCase="[UNK]"	, _lowerCamelCase="[PAD]"	, _lowerCamelCase="[MASK]"	, _lowerCamelCase=False	, **_lowerCamelCase	, ):
										super().__init__(
										    _lowerCamelCase	, _lowerCamelCase	, tokenizer_file=_lowerCamelCase	, errors=_lowerCamelCase	, bos_token=_lowerCamelCase	, eos_token=_lowerCamelCase	, unk_token=_lowerCamelCase	, sep_token=_lowerCamelCase	, cls_token=_lowerCamelCase	, pad_token=_lowerCamelCase	, mask_token=_lowerCamelCase	, add_prefix_space=_lowerCamelCase	, **_lowerCamelCase	, )
										lowerCamelCase__					=kwargs.pop("add_bos_token"	, _lowerCamelCase      )

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

										lowerCamelCase__					=add_prefix_space

				@property
				def 		_a	(      self      ):
										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 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=AddedToken(_lowerCamelCase	, lstrip=_lowerCamelCase	, rstrip=_lowerCamelCase      ) if isinstance(_lowerCamelCase	, _lowerCamelCase      ) else value
										lowerCamelCase__					=value

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None      ):
										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowerCamelCase__					=[self.cls_token_id]
										lowerCamelCase__					=[self.sep_token_id]
										return cls + token_ids_a + sep + token_ids_a + sep

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None      ):
										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      ) * [0] + len(token_ids_a + sep      ) * [1]

				def 		_a	(      self	, *_lowerCamelCase	, **_lowerCamelCase      ):
										lowerCamelCase__					=kwargs.get("is_split_into_words"	, _lowerCamelCase      )
										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(*_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, *_lowerCamelCase	, **_lowerCamelCase      ):
										lowerCamelCase__					=kwargs.get("is_split_into_words"	, _lowerCamelCase      )

										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(*_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None      ):
										lowerCamelCase__					=self._tokenizer.model.save(_lowerCamelCase	, name=_lowerCamelCase      )
										return tuple(_lowerCamelCase      )






				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(_lowerCamelCase	, add_special_tokens=_lowerCamelCase      ) + [self.eos_token_id]      )

										if len(_lowerCamelCase      ) > self.model_max_length:
																lowerCamelCase__					=input_ids[-self.model_max_length :]
										return input_ids



"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, ):
										super().__init__()

										self.register_modules(vae=_lowerCamelCase	, text_encoder=_lowerCamelCase	, tokenizer=_lowerCamelCase	, unet=_lowerCamelCase	, scheduler=_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase      ):
										with torch.no_grad():
																lowerCamelCase__					=self.vae.encode(tfms.ToTensor()(_lowerCamelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
																lowerCamelCase__					=0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=(1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
																lowerCamelCase__					=self.vae.decode(_lowerCamelCase      ).sample
										lowerCamelCase__					=(img / 2 + 0.5).clamp(0	, 1      )
										lowerCamelCase__					=img.detach().cpu().permute(0	, 2	, 3	, 1      ).numpy()
										lowerCamelCase__					=(img * 255).round().astype("uint8"      )
										return Image.fromarray(img[0]      )

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=self.tokenizer(
										    _lowerCamelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

										lowerCamelCase__					=self.tokenizer(
										    ""	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

										return torch.cat([uncond_embedding, text_embedding]      )






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = 0.3	, _lowerCamelCase = 0.6	, _lowerCamelCase = 0.5	, _lowerCamelCase = 42	, _lowerCamelCase = 50	, _lowerCamelCase = 7.5	, ):
										lowerCamelCase__					=steps - int(kmin * steps      )
										lowerCamelCase__					=steps - int(kmax * steps      )

										lowerCamelCase__					=self.prep_text(_lowerCamelCase      )

										self.scheduler.set_timesteps(_lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					=img.size
										lowerCamelCase__					=self.encode(_lowerCamelCase      )

										torch.manual_seed(_lowerCamelCase      )
										lowerCamelCase__					=torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)	, ).to(self.device      )

										lowerCamelCase__					=self.scheduler.add_noise(
										    _lowerCamelCase	, _lowerCamelCase	, timesteps=self.scheduler.timesteps[tmax]	, )

										lowerCamelCase__					=torch.cat([latents] * 2      )

										lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, self.scheduler.timesteps[tmax]      )

										with torch.no_grad():
																lowerCamelCase__					=self.unet(
																    _lowerCamelCase	, self.scheduler.timesteps[tmax]	, encoder_hidden_states=_lowerCamelCase	, ).sample

										lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
										lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

										lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, self.scheduler.timesteps[tmax]	, _lowerCamelCase      ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
																if i > tmax:
																						if i < tmin:  # layout generation phase
																												lowerCamelCase__					=self.scheduler.add_noise(
																												    _lowerCamelCase	, _lowerCamelCase	, timesteps=_lowerCamelCase	, )

																												lowerCamelCase__					=(mix_factor * latents) + (
																												    1 - mix_factor
																												) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																												lowerCamelCase__					=torch.cat([input] * 2      )

																						else:  # content generation phase
																												lowerCamelCase__					=torch.cat([latents] * 2      )

																						lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, _lowerCamelCase      )

																						with torch.no_grad():
																												lowerCamelCase__					=self.unet(
																												    _lowerCamelCase	, _lowerCamelCase	, encoder_hidden_states=_lowerCamelCase	, ).sample

																						lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
																						lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

																						lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ).prev_sample

										return self.decode(_lowerCamelCase      )


