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    lowercase_    (     __snake_case		):



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

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



							def        UpperCamelCase       (						self      ,				lowercase_				):
													with torch.no_grad():
																			_snake_case						:     Optional[Any]				=    self.vae.encode(tfms.ToTensor()(lowercase_				).unsqueeze(0				).to(self.device				) * 2 - 1				)
																			_snake_case						:     Tuple				=    0.18_215 * latent.latent_dist.sample()
													return latent



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     List[str]				=    (1 / 0.18_215) * latent
													with torch.no_grad():
																			_snake_case						:     Tuple				=    self.vae.decode(lowercase_				).sample
													_snake_case						:     str				=    (img / 2 + 0.5).clamp(0      ,				1				)
													_snake_case						:     Optional[Any]				=    img.detach().cpu().permute(0      ,				2      ,				3      ,				1				).numpy()
													_snake_case						:     Dict				=    (img * 255).round().astype("uint8"				)
													return Image.fromarray(img[0]				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     str				=    self.tokenizer(
													    lowercase_      ,				padding="max_length"      ,				max_length=self.tokenizer.model_max_length      ,				truncation=lowercase_      ,				return_tensors="pt"      ,				)

													_snake_case						:     Optional[Any]				=    self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

													_snake_case						:     Dict				=    self.tokenizer(
													    ""      ,				padding="max_length"      ,				max_length=self.tokenizer.model_max_length      ,				truncation=lowercase_      ,				return_tensors="pt"      ,				)

													_snake_case						:     Tuple				=    self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

													return torch.cat([uncond_embedding, text_embedding]				)



							def __call__(						self      ,				lowercase_      ,				lowercase_      ,				lowercase_ = 0.3      ,				lowercase_ = 0.6      ,				lowercase_ = 0.5      ,				lowercase_ = 42      ,				lowercase_ = 50      ,				lowercase_ = 7.5      ,				):
													_snake_case						:     str				=    steps - int(kmin * steps				)
													_snake_case						:     Tuple				=    steps - int(kmax * steps				)

													_snake_case						:     Union[str, Any]				=    self.prep_text(lowercase_				)

													self.scheduler.set_timesteps(lowercase_				)

													_snake_case				,_snake_case						:     List[Any]				=    img.size
													_snake_case						:     Optional[Any]				=    self.encode(lowercase_				)

													torch.manual_seed(lowercase_				)
													_snake_case						:     str				=    torch.randn(
													    (1, self.unet.config.in_channels, height // 8, width // 8)      ,				).to(self.device				)

													_snake_case						:     Any				=    self.scheduler.add_noise(
													    lowercase_      ,				lowercase_      ,				timesteps=self.scheduler.timesteps[tmax]      ,				)

													_snake_case						:     Tuple				=    torch.cat([latents] * 2				)

													_snake_case						:     Optional[int]				=    self.scheduler.scale_model_input(lowercase_      ,				self.scheduler.timesteps[tmax]				)

													with torch.no_grad():
																			_snake_case						:     List[Any]				=    self.unet(
																			    lowercase_      ,				self.scheduler.timesteps[tmax]      ,				encoder_hidden_states=lowercase_      ,				).sample

													_snake_case				,_snake_case						:     Dict				=    pred.chunk(2				)
													_snake_case						:     str				=    pred_uncond + guidance_scale * (pred_text - pred_uncond)

													_snake_case						:     int				=    self.scheduler.step(lowercase_      ,				self.scheduler.timesteps[tmax]      ,				lowercase_				).prev_sample

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

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

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

																									_snake_case						:     Tuple				=    self.scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																									with torch.no_grad():
																															_snake_case						:     str				=    self.unet(
																															    lowercase_      ,				lowercase_      ,				encoder_hidden_states=lowercase_      ,				).sample

																									_snake_case				,_snake_case						:     List[Any]				=    pred.chunk(2				)
																									_snake_case						:     int				=    pred_uncond + guidance_scale * (pred_text - pred_uncond)

																									_snake_case						:     Optional[Any]				=    self.scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				).prev_sample

													return self.decode(lowercase_				)





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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 a_      						=   None

a_      						=   logging.get_logger(__name__)

a_      						=   {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

a_      						=   {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

a_      						=   {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

a_      						=   {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       int			=    VOCAB_FILES_NAMES
      lowerCAmelCase__	:       Any			=    PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__	:       Optional[Any]			=    PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__	:       int			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__	:       Dict			=    DebertaVaTokenizer







      def __init__(   self    :       int       ,				snake_case    :       int=None       ,				snake_case    :       Optional[int]=None       ,				snake_case    :       Any=False       ,				snake_case    :       Optional[int]=False       ,				snake_case    :       Dict="[CLS]"       ,				snake_case    :       Any="[SEP]"       ,				snake_case    :       Union[str, Any]="[UNK]"       ,				snake_case    :       List[Any]="[SEP]"       ,				snake_case    :       Union[str, Any]="[PAD]"       ,				snake_case    :       Tuple="[CLS]"       ,				snake_case    :       Dict="[MASK]"       ,				**snake_case    :       int       ,				):
          super().__init__(
              snake_case       ,				tokenizer_file=snake_case       ,				do_lower_case=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       ,				split_by_punct=snake_case       ,				**snake_case       ,				)

          __UpperCamelCase     						=  do_lower_case
          __UpperCamelCase     						=  split_by_punct
          __UpperCamelCase     						=  vocab_file
          __UpperCamelCase     						=  False if not self.vocab_file else True







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       int       ,				snake_case    :       Tuple=None ):

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







      def 					snake_case		(   self    :       Any       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[Any]=None       ,				snake_case    :       Union[str, Any]=False ):

          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=snake_case       ,				token_ids_a=snake_case       ,				already_has_special_tokens=snake_case )

          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 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[Any]=None ):
          __UpperCamelCase     						=  [self.sep_token_id]
          __UpperCamelCase     						=  [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 					snake_case		(   self    :       List[str]       ,				snake_case    :       str       ,				snake_case    :       Optional[str] = None ):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.''' )

          if not os.path.isdir(snake_case ):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
              return
          __UpperCamelCase     						=  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,)






