




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     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Optional[Any]    		=   'char'
      UpperCAmelCase__      :	List[str]    		=   'bpe'
      UpperCAmelCase__      :	int    		=   'wp'



UpperCAmelCase_									=	(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	List[Any]    		=   ['image_processor', 'char_tokenizer']
      UpperCAmelCase__      :	Tuple    		=   'ViTImageProcessor'
      UpperCAmelCase__      :	Optional[int]    		=   'MgpstrTokenizer'
      def __init__(				self: Union[str, Any]    ,       UpperCamelCase_: str=None    ,       UpperCamelCase_: Optional[int]=None    ,       **UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                        """ instead."""    ,       UpperCamelCase_    ,       )
                    __lowerCamelCase   =						kwargs.pop("""feature_extractor"""   )

             __lowerCamelCase   =						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`."""   )

             __lowerCamelCase   =						tokenizer
             __lowerCamelCase   =						AutoTokenizer.from_pretrained("""gpt2"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained("""bert-base-uncased"""   )

             super().__init__(UpperCamelCase_    ,       UpperCamelCase_   )
      def __call__(				self: Tuple    ,       UpperCamelCase_: Optional[int]=None    ,       UpperCamelCase_: List[str]=None    ,       UpperCamelCase_: Any=None    ,       **UpperCamelCase_: Any   ):
             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:
                    __lowerCamelCase   =						self.image_processor(UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       **UpperCamelCase_   )
             if text is not None:
                    __lowerCamelCase   =						self.char_tokenizer(UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       **UpperCamelCase_   )

             if text is None:
                    return inputs
             elif images is None:
                    return encodings
             else:
                    __lowerCamelCase   =						encodings["""input_ids"""]
                    return inputs
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: int   ):
             __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						sequences
             __lowerCamelCase   =						char_preds.size(0   )

             __lowerCamelCase,   __lowerCamelCase   =						self._decode_helper(UpperCamelCase_    ,       """char"""   )
             __lowerCamelCase,   __lowerCamelCase   =						self._decode_helper(UpperCamelCase_    ,       """bpe"""   )
             __lowerCamelCase,   __lowerCamelCase   =						self._decode_helper(UpperCamelCase_    ,       """wp"""   )

             __lowerCamelCase   =						[]
             __lowerCamelCase   =						[]
             for i in range(UpperCamelCase_   ):
                    __lowerCamelCase   =						[char_scores[i], bpe_scores[i], wp_scores[i]]
                    __lowerCamelCase   =						[char_strs[i], bpe_strs[i], wp_strs[i]]
                    __lowerCamelCase   =						scores.index(max(UpperCamelCase_   )   )
                    final_strs.append(strs[max_score_index]   )
                    final_scores.append(scores[max_score_index]   )

             __lowerCamelCase   =						{}
             __lowerCamelCase   =						final_strs
             __lowerCamelCase   =						final_scores
             __lowerCamelCase   =						char_strs
             __lowerCamelCase   =						bpe_strs
             __lowerCamelCase   =						wp_strs
             return out
      def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Optional[Any]   ):
             if format == DecodeType.CHARACTER:
                    __lowerCamelCase   =						self.char_decode
                    __lowerCamelCase   =						1
                    __lowerCamelCase   =						"""[s]"""
             elif format == DecodeType.BPE:
                    __lowerCamelCase   =						self.bpe_decode
                    __lowerCamelCase   =						2
                    __lowerCamelCase   =						"""#"""
             elif format == DecodeType.WORDPIECE:
                    __lowerCamelCase   =						self.wp_decode
                    __lowerCamelCase   =						1_02
                    __lowerCamelCase   =						"""[SEP]"""
             else:
                    raise ValueError(F'Format {format} is not supported.'   )

             __lowerCamelCase,   __lowerCamelCase   =						[], []
             __lowerCamelCase   =						pred_logits.size(0   )
             __lowerCamelCase   =						pred_logits.size(1   )
             __lowerCamelCase,   __lowerCamelCase   =						pred_logits.topk(1    ,       dim=-1    ,       largest=UpperCamelCase_    ,       sorted=UpperCamelCase_   )
             __lowerCamelCase   =						preds_index.view(-1    ,       UpperCamelCase_   )[:, 1:]
             __lowerCamelCase   =						decoder(UpperCamelCase_   )
             __lowerCamelCase,   __lowerCamelCase   =						torch.nn.functional.softmax(UpperCamelCase_    ,       dim=2   ).max(dim=2   )
             __lowerCamelCase   =						preds_max_prob[:, 1:]

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

             return dec_strs, conf_scores
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, Any]   ):
             __lowerCamelCase   =						[seq.replace(""" """    ,       """"""   ) for seq in self.char_tokenizer.batch_decode(UpperCamelCase_   )]
             return decode_strs
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Tuple   ):
             return self.bpe_tokenizer.batch_decode(UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						[seq.replace(""" """    ,       """"""   ) for seq in self.wp_tokenizer.batch_decode(UpperCamelCase_   )]
             return decode_strs







from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_									=	[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
UpperCAmelCase_									=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						0
                     if i > 0 and j > 0:
                            neighbour_count += cells[i - 1][j - 1]
                     if i > 0:
                            neighbour_count += cells[i - 1][j]
                     if i > 0 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i - 1][j + 1]
                     if j > 0:
                            neighbour_count += cells[i][j - 1]
                     if j < len(cells[i]	) - 1:
                            neighbour_count += cells[i][j + 1]
                     if i < len(A__	) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(A__	) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(A__	) - 1 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i + 1][j + 1]

                     # Rules of the game of life (excerpt from Wikipedia):
                     # 1. Any live cell with two or three live neighbours survives.
                     # 2. Any dead cell with three live neighbours becomes a live cell.
                     # 3. All other live cells die in the next generation.
                     #    Similarly, all other dead cells stay dead.
                     __lowerCamelCase   =						cells[i][j] == 1
                     if (
                         (alive and 2 <= neighbour_count <= 3)
                         or not alive
                         and neighbour_count == 3
                     ):
                            next_generation_row.append(1	)
                     else:
                            next_generation_row.append(0	)

              next_generation.append(A__	)
       return next_generation




def 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for _ in range(A__	):
              # Create output image
              __lowerCamelCase   =						Image.new("""RGB"""  ,			(len(cells[0]	), len(A__	))	)
              __lowerCamelCase   =						img.load()

              # Save cells to image
              for x in range(len(A__	)	):
                     for y in range(len(cells[0]	)	):
                            __lowerCamelCase   =						255 - cells[y][x] * 255
                            __lowerCamelCase   =						(colour, colour, colour)

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


if __name__ == "__main__":
       UpperCAmelCase_									=	generate_images(GLIDER, 16)
       images[0].save('out.gif', save_all=True, append_images=images[1:])

