




'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_    	=				logging.get_logger(__name__)

UpperCamelCase_    	=				"▁"

UpperCamelCase_    	=				{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase_    	=				{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCamelCase_    	=				{
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
UpperCamelCase_    	=				["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[str]        =						VOCAB_FILES_NAMES
       A						:				str        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				List[str]        =						PRETRAINED_VOCAB_FILES_MAP
       A						:				Dict        =						['''input_ids''', '''attention_mask''']

       A						:				List[int]        =						[]
       A						:				List[int]        =						[]







       def __init__(      self,							A,							A="<s>",							A="</s>",							A="</s>",							A="<s>",							A="<unk>",							A="<pad>",							A="<mask>",							A=None,							A=None,							A=None,							A = None,							A=None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						AddedToken(A,							lstrip=A,							rstrip=A  ) if isinstance(A,							A  ) else mask_token

             SCREAMING_SNAKE_CASE   :      Tuple    					=						{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=A,							eos_token=A,							unk_token=A,							sep_token=A,							cls_token=A,							pad_token=A,							mask_token=A,							tokenizer_file=A,							src_lang=A,							tgt_lang=A,							additional_special_tokens=A,							sp_model_kwargs=self.sp_model_kwargs,							**A,							)

             SCREAMING_SNAKE_CASE   :      int    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.Load(str(A  )  )
             SCREAMING_SNAKE_CASE   :      str    					=						vocab_file

             # Original fairseq vocab and spm vocab must be "aligned":
             # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
             # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
             # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
             # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

             # Mimic fairseq token-to-id alignment for the first 4 token
             SCREAMING_SNAKE_CASE   :      str    					=						{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

             # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						1

             SCREAMING_SNAKE_CASE   :      Dict    					=						len(self.sp_model  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{
                 code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A  )
             }
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{v: k for k, v in self.lang_code_to_id.items()}
             SCREAMING_SNAKE_CASE   :      int    					=						len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

             self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
             SCREAMING_SNAKE_CASE   :      str    					=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
             SCREAMING_SNAKE_CASE   :      Tuple    					=						list(self.lang_code_to_id.keys()  )

             if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   self._additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in self._additional_special_tokens]  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						src_lang if src_lang is not None else 'en_XX'
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.lang_code_to_id[self._src_lang]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						tgt_lang
             self.set_src_lang_special_tokens(self._src_lang  )







       def __getstate__(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.__dict__.copy()
             SCREAMING_SNAKE_CASE   :      str    					=						None
             SCREAMING_SNAKE_CASE   :      str    					=						self.sp_model.serialized_model_proto()
             return state







       def __setstate__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						d

             # for backward compatibility
             if not hasattr(self,							'sp_model_kwargs'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						{}

             SCREAMING_SNAKE_CASE   :      Tuple    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self._src_lang







       @src_lang.setter
       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						new_src_lang
             self.set_src_lang_special_tokens(self._src_lang  )







       def        UpperCamelCase_							(      self,							A,							A = None,							A = False  ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[1] * len(self.prefix_tokens  )
             SCREAMING_SNAKE_CASE   :      int    					=						[1] * len(self.suffix_tokens  )
             if token_ids_a is None:
                   return prefix_ones + ([0] * len(A  )) + suffix_ones
             return prefix_ones + ([0] * len(A  )) + ([0] * len(A  )) + suffix_ones







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[self.sep_token_id]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[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]







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							**A  ):







             '''simple docstring'''







             if src_lang is None or tgt_lang is None:
                   raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'  )
             SCREAMING_SNAKE_CASE   :      int    					=						src_lang
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self(A,							add_special_tokens=A,							return_tensors=A,							**A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.convert_tokens_to_ids(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						tgt_lang_id
             return inputs







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{self.convert_ids_to_tokens(A  ): i for i in range(self.vocab_size  )}
             vocab.update(self.added_tokens_encoder  )
             return vocab







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.encode(A,							out_type=A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.sp_model.PieceToId(A  )

             # Need to return unknown token if the SP model returned 0
             return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if index in self.fairseq_ids_to_tokens:
                   return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						''.join(A  ).replace(A,							' '  ).strip()
             return out_string







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             if not os.path.isdir(A  ):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
                   return
             SCREAMING_SNAKE_CASE   :      Dict    					=						os.path.join(
                 A,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

             if os.path.abspath(self.vocab_file  ) != os.path.abspath(A  ) and os.path.isfile(self.vocab_file  ):
                   copyfile(self.vocab_file,							A  )
             elif not os.path.isfile(self.vocab_file  ):
                   with open(A,							'wb'  ) as fi:
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.sp_model.serialized_model_proto()
                         fi.write(A  )

             return (out_vocab_file,)







       def        UpperCamelCase_							(      self,							A,							A = "en_XX",							A = None,							A = "ro_RO",							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						src_lang
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tgt_lang
             return super().prepare_seqaseq_batch(A,							A,							**A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.set_src_lang_special_tokens(self.src_lang  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.set_tgt_lang_special_tokens(self.tgt_lang  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.lang_code_to_id[src_lang]
             SCREAMING_SNAKE_CASE   :      int    					=						[]
             SCREAMING_SNAKE_CASE   :      int    					=						[self.eos_token_id, self.cur_lang_code]







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.lang_code_to_id[lang]
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Dict    					=						[self.eos_token_id, self.cur_lang_code]



"""simple docstring"""






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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase       :			Optional[Any]   	=logging.get_logger(__name__)  # pylint: disable=invalid-name


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Optional[Any]:
      '''simple docstring'''


      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""   ,						lowerCAmelCase__   ,						)
      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return image
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [image]

      if isinstance(image[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      image[0].size
            lowercase				,				lowercase								=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            lowercase								=      [np.array(i.resize((w, h)   ,						resample=PIL_INTERPOLATION["""lanczos"""]						)						)[None, :] for i in image]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      np.array(lowerCAmelCase__						).astype(np.floataa						) / 255.0
            lowercase								=      image.transpose(0   ,						3   ,						1   ,						2						)
            lowercase								=      2.0 * image - 1.0
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(image[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return image


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Dict:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return mask
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [mask]

      if isinstance(mask[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      mask[0].size
            lowercase				,				lowercase								=      (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
            lowercase								=      [np.array(m.convert("""L"""						).resize((w, h)   ,						resample=PIL_INTERPOLATION["""nearest"""]						)						)[None, :] for m in mask]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      mask.astype(np.floataa						) / 255.0
            lowercase								=      0
            lowercase								=      1
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(mask[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return mask






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  UNetaDModel
      snake_case__     :  RePaintScheduler

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            self.register_modules(unet=__lowerCAmelCase						,			scheduler=__lowerCAmelCase		)







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 250						,			__lowerCAmelCase = 0.0						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			):







            """simple docstring"""



            lowercase								=      image

            lowercase								=      _preprocess_image(__lowerCAmelCase		)
            lowercase								=      original_image.to(device=self.device						,			dtype=self.unet.dtype		)
            lowercase								=      _preprocess_mask(__lowerCAmelCase		)
            lowercase								=      mask_image.to(device=self.device						,			dtype=self.unet.dtype		)

            lowercase								=      original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) and len(__lowerCAmelCase		) != batch_size:
                  raise ValueError(
                      f'You have passed a list of generators of length {len(__lowerCAmelCase		)}, but requested an effective batch'
                      f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

            lowercase								=      original_image.shape
            lowercase								=      randn_tensor(__lowerCAmelCase						,			generator=__lowerCAmelCase						,			device=self.device						,			dtype=self.unet.dtype		)

            # set step values
            self.scheduler.set_timesteps(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			self.device		)
            lowercase								=      eta

            lowercase								=      self.scheduler.timesteps[0] + 1
            lowercase								=      generator[0] if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
                  if t < t_last:
                        # predict the noise residual
                        lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample
                        # compute previous image: x_t -> x_t-1
                        lowercase								=      self.scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

                  else:
                        # compute the reverse: x_t-1 -> x_t
                        lowercase								=      self.scheduler.undo_step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      t

            lowercase								=      (image / 2 + 0.5).clamp(0						,			1		)
            lowercase								=      image.cpu().permute(0						,			2						,			3						,			1		).numpy()
            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



