



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
		import torch

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Dict      =					"char"
						__lowerCAmelCase					:Optional[int]      =					"bpe"
						__lowerCAmelCase					:int      =					"wp"




_lowercase		:				Union[str, Any]      		=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					["image_processor", "char_tokenizer"]
						__lowerCAmelCase					:List[Any]      =					"ViTImageProcessor"
						__lowerCAmelCase					:Union[str, Any]      =					"MgpstrTokenizer"

						def __init__(       self ,       __lowercase=None ,       __lowercase=None ,       **__lowercase  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Optional[int]							=  None
									if "feature_extractor" in kwargs:
												warnings.warn(
												    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
												    """ instead.""" ,       __lowercase ,       )
												a__       :						Dict							=  kwargs.pop("""feature_extractor"""  )

									a__       :						int							=  image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
												raise ValueError("""You need to specify an `image_processor`."""  )
									if tokenizer is None:
												raise ValueError("""You need to specify a `tokenizer`."""  )

									a__       :						List[str]							=  tokenizer
									a__       :						Any							=  AutoTokenizer.from_pretrained("""gpt2"""  )
									a__       :						str							=  AutoTokenizer.from_pretrained("""bert-base-uncased"""  )

									super().__init__(__lowercase ,       __lowercase  )

						def __call__(       self ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       **__lowercase  ) ->				Any:
									"""simple docstring"""




									if images is None and text is None:
												raise ValueError("""You need to specify either an `images` or `text` input to process."""  )

									if images is not None:
												a__       :						Any							=  self.image_processor(__lowercase ,       return_tensors=__lowercase ,       **__lowercase  )
									if text is not None:
												a__       :						List[Any]							=  self.char_tokenizer(__lowercase ,       return_tensors=__lowercase ,       **__lowercase  )

									if text is None:
												return inputs
									elif images is None:
												return encodings
									else:
												a__       :						int							=  encodings["""input_ids"""]
												return inputs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__	,						a__	,						a__       :						str							=  sequences
									a__       :						Tuple							=  char_preds.size(0  )

									a__	,						a__       :						int							=  self._decode_helper(__lowercase ,       """char"""  )
									a__	,						a__       :						List[Any]							=  self._decode_helper(__lowercase ,       """bpe"""  )
									a__	,						a__       :						List[str]							=  self._decode_helper(__lowercase ,       """wp"""  )

									a__       :						List[str]							=  []
									a__       :						List[str]							=  []
									for i in range(__lowercase  ):
												a__       :						Any							=  [char_scores[i], bpe_scores[i], wp_scores[i]]
												a__       :						Optional[int]							=  [char_strs[i], bpe_strs[i], wp_strs[i]]
												a__       :						Tuple							=  scores.index(max(__lowercase  )  )
												final_strs.append(strs[max_score_index]  )
												final_scores.append(scores[max_score_index]  )

									a__       :						Any							=  {}
									a__       :						List[Any]							=  final_strs
									a__       :						List[str]							=  final_scores
									a__       :						str							=  char_strs
									a__       :						Union[str, Any]							=  bpe_strs
									a__       :						Any							=  wp_strs
									return out

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									if format == DecodeType.CHARACTER:
												a__       :						Optional[Any]							=  self.char_decode
												a__       :						Union[str, Any]							=  1
												a__       :						str							=  """[s]"""
									elif format == DecodeType.BPE:
												a__       :						List[str]							=  self.bpe_decode
												a__       :						Optional[int]							=  2
												a__       :						List[Any]							=  """#"""
									elif format == DecodeType.WORDPIECE:
												a__       :						int							=  self.wp_decode
												a__       :						Optional[int]							=  1_0_2
												a__       :						List[str]							=  """[SEP]"""
									else:
												raise ValueError(F'''Format {format} is not supported.'''  )

									a__	,						a__       :						int							=  [], []
									a__       :						Optional[Any]							=  pred_logits.size(0  )
									a__       :						Tuple							=  pred_logits.size(1  )
									a__	,						a__       :						Any							=  pred_logits.topk(1 ,       dim=-1 ,       largest=__lowercase ,       sorted=__lowercase  )
									a__       :						Union[str, Any]							=  preds_index.view(-1 ,       __lowercase  )[:, 1:]
									a__       :						Tuple							=  decoder(__lowercase  )
									a__	,						a__       :						Optional[int]							=  torch.nn.functional.softmax(__lowercase ,       dim=2  ).max(dim=2  )
									a__       :						Union[str, Any]							=  preds_max_prob[:, 1:]

									for index in range(__lowercase  ):
												a__       :						List[Any]							=  preds_str[index].find(__lowercase  )
												a__       :						Optional[int]							=  preds_str[index][:pred_eos]
												a__       :						Optional[int]							=  preds_index[index].cpu().tolist()
												a__       :						Any							=  pred_index.index(__lowercase  ) if eos_token in pred_index else -1
												a__       :						int							=  preds_max_prob[index][: pred_eos_index + 1]
												a__       :						Optional[Any]							=  pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
												dec_strs.append(__lowercase  )
												conf_scores.append(__lowercase  )

									return dec_strs, conf_scores

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Tuple							=  [seq.replace(""" """ ,       """"""  ) for seq in self.char_tokenizer.batch_decode(__lowercase  )]
									return decode_strs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




									return self.bpe_tokenizer.batch_decode(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Dict							=  [seq.replace(""" """ ,       """"""  ) for seq in self.wp_tokenizer.batch_decode(__lowercase  )]
									return decode_strs









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




